face_generation_gan/dlnd_face_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Generation\n",
    "In this project, you'll use generative adversarial networks to generate new images of faces.\n",
    "### Get the Data\n",
    "You'll be using two datasets in this project:\n",
    "- MNIST\n",
    "- CelebA\n",
    "\n",
    "Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA.  Running the GANs on MNIST will allow you to see how well your model trains sooner.\n",
    "\n",
    "If you're using [FloydHub](https://www.floydhub.com/), set `data_dir` to \"/input\" and use the [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) \"R5KrjnANiKVhLWAkpXhNBe\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found mnist Data\n",
      "Found celeba Data\n"
     ]
    }
   ],
   "source": [
    "#data_dir = './data'\n",
    "data_dir = '/input'\n",
    "\n",
    "# FloydHub - Use with data ID \"R5KrjnANiKVhLWAkpXhNBe\"\n",
    "#data_dir = '/input'\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import helper\n",
    "\n",
    "helper.download_extract('mnist', data_dir)\n",
    "helper.download_extract('celeba', data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Data\n",
    "### MNIST\n",
    "As you're aware, the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset contains images of handwritten digits. You can view the first number of examples by changing `show_n_images`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd45b2809b0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noF4BCBgOQkQAQ6EQE+F
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd45b1bc518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_n_images = 25\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "%matplotlib inline\n",
    "import os\n",
    "from glob import glob\n",
    "from matplotlib import pyplot\n",
    "\n",
    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')\n",
    "pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CelebA\n",
    "The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations.  Since you're going to be generating faces, you won't need the annotations.  You can view the first number of examples by changing `show_n_images`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd45b17db70>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGD/n/PRnWYZY0xyLxoV
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd484624c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_n_images = 25\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')\n",
    "pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "Since the project's main focus is on building the GANs, we'll preprocess the data for you.  The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images.  The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.\n",
    "\n",
    "The MNIST images are black and white images with a single [color channel](https://en.wikipedia.org/wiki/Channel_(digital_image%29) while the CelebA images have [3 color channels (RGB color channel)](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images).\n",
    "## Build the Neural Network\n",
    "You'll build the components necessary to build a GANs by implementing the following functions below:\n",
    "- `model_inputs`\n",
    "- `discriminator`\n",
    "- `generator`\n",
    "- `model_loss`\n",
    "- `model_opt`\n",
    "- `train`\n",
    "\n",
    "### Check the Version of TensorFlow and Access to GPU\n",
    "This will check to make sure you have the correct version of TensorFlow and access to a GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow Version: 1.2.1\n",
      "Default GPU Device: /gpu:0\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "from distutils.version import LooseVersion\n",
    "import warnings\n",
    "import tensorflow as tf\n",
    "\n",
    "# Check TensorFlow Version\n",
    "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)\n",
    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
    "\n",
    "# Check for a GPU\n",
    "if not tf.test.gpu_device_name():\n",
    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
    "else:\n",
    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input\n",
    "Implement the `model_inputs` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n",
    "- Real input images placeholder with rank 4 using `image_width`, `image_height`, and `image_channels`.\n",
    "- Z input placeholder with rank 2 using `z_dim`.\n",
    "- Learning rate placeholder with rank 0.\n",
    "\n",
    "Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:tensorflow:==================================\n",
      "Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):\n",
      "<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>\n",
      "If you want to mark it as used call its \"mark_used()\" method.\n",
      "It was originally created here:\n",
      "['File \"/usr/local/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\\n    \"__main__\", mod_spec)', 'File \"/usr/local/lib/python3.5/runpy.py\", line 85, in _run_code\\n    exec(code, run_globals)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\\n    app.launch_new_instance()', 'File \"/usr/local/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\\n    app.start()', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 477, in start\\n    ioloop.IOLoop.instance().start()', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\\n    super(ZMQIOLoop, self).start()', 'File \"/usr/local/lib/python3.5/site-packages/tornado/ioloop.py\", line 888, in start\\n    handler_func(fd_obj, events)', 'File \"/usr/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n    return fn(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\\n    self._handle_recv()', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\\n    self._run_callback(callback, msg)', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\\n    callback(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n    return fn(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\\n    return self.dispatch_shell(stream, msg)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\\n    handler(stream, idents, msg)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\\n    user_expressions, allow_stdin)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\\n    interactivity=interactivity, compiler=compiler, result=result)', 'File \"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2808, in run_ast_nodes\\n    if self.run_code(code, result):', 'File \"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File \"<ipython-input-6-60e8f0e9d266>\", line 22, in <module>\\n    tests.test_model_inputs(model_inputs)', 'File \"/output/problem_unittests.py\", line 12, in func_wrapper\\n    result = func(*args)', 'File \"/output/problem_unittests.py\", line 68, in test_model_inputs\\n    _check_input(learn_rate, [], \\'Learning Rate\\')', 'File \"/output/problem_unittests.py\", line 34, in _check_input\\n    _assert_tensor_shape(tensor, shape, \\'Real Input\\')', 'File \"/output/problem_unittests.py\", line 20, in _assert_tensor_shape\\n    assert tf.assert_rank(tensor, len(shape), message=\\'{} has wrong rank\\'.format(display_name))', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 617, in assert_rank\\n    dynamic_condition, data, summarize)', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 571, in _assert_rank_condition\\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 170, in wrapped\\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 139, in _add_should_use_warn
      "==================================\n",
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "import problem_unittests as tests\n",
    "\n",
    "def model_inputs(image_width, image_height, image_channels, z_dim):\n",
    "    \"\"\"\n",
    "    Create the model inputs\n",
    "    :param image_width: The input image width\n",
    "    :param image_height: The input image height\n",
    "    :param image_channels: The number of image channels\n",
    "    :param z_dim: The dimension of Z\n",
    "    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)\n",
    "    \"\"\"\n",
    "    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')\n",
    "    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='inputs_z')\n",
    "    learn_r = tf.placeholder(tf.float32, name='lr')\n",
    "\n",
    "    return inputs_real, inputs_z, learn_r\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_inputs(model_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "alpha = 0.1 # for leaky relu\n",
    "print_every = 10\n",
    "show_every = 100 #change to 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discriminator\n",
    "Implement `discriminator` to create a discriminator neural network that discriminates on `images`.  This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"discriminator\" to allow the variables to be reused.  The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def discriminator(images, reuse=False):\n",
    "    \"\"\"\n",
    "    Create the discriminator network\n",
    "    :param images: Tensor of input image(s)\n",
    "    :param reuse: Boolean if the weights should be reused\n",
    "    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)\n",
    "    \"\"\"\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        \n",
    "        # Input layer is 28x28x3\n",
    "        x1 = tf.layers.conv2d(images, 64, 5, strides=1, padding='valid')\n",
    "        relu1 = tf.maximum(alpha * x1, x1)\n",
    "        #print(relu1)\n",
    "        # 24x24x64\n",
    "        \n",
    "        x2 = tf.layers.conv2d(relu1, 128, 5, strides=1, padding='valid')\n",
    "        bn2 = tf.layers.batch_normalization(x2, training=True)\n",
    "        relu2 = tf.maximum(alpha * bn2, bn2)\n",
    "        #print(relu2)\n",
    "        # 20x20x128\n",
    "        \n",
    "        x3 = tf.layers.conv2d(relu2, 256, 5, strides=1, padding='valid')\n",
    "        bn3 = tf.layers.batch_normalization(x3, training=True)\n",
    "        relu3 = tf.maximum(alpha * bn3, bn3)\n",
    "        #print(relu3)\n",
    "        # 16x16x256\n",
    "        \n",
    "        x4 = tf.layers.conv2d(relu3, 512, 5, strides=2, padding='valid')\n",
    "        bn4 = tf.layers.batch_normalization(x4, training=True)\n",
    "        relu4 = tf.maximum(alpha * bn4, bn4)\n",
    "        #print(relu4)\n",
    "        # 6x6x512\n",
    "\n",
    "        # Flatten it\n",
    "        flat = tf.reshape(relu4, (-1, 6*6*512))\n",
    "        logits = tf.layers.dense(flat, 1)\n",
    "        #print(flat)\n",
    "        out = tf.sigmoid(logits)\n",
    "       \n",
    "        \n",
    "\n",
    "    return out, logits\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(discriminator, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generator\n",
    "Implement `generator` to generate an image using `z`. This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"generator\" to allow the variables to be reused. The function should return the generated 28 x 28 x `out_channel_dim` images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def generator(z, out_channel_dim, is_train=True):\n",
    "    \"\"\"\n",
    "    Create the generator network\n",
    "    :param z: Input z\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :param is_train: Boolean if generator is being used for training\n",
    "    :return: The tensor output of the generator\n",
    "    \"\"\"\n",
    " \n",
    "    with tf.variable_scope('generator', reuse=not is_train):\n",
    "      \n",
    "        # First fully connected layer\n",
    "        fc = tf.layers.dense(z, 7*7*1024)\n",
    "        fc = tf.reshape(fc, (-1, 7, 7, 1024))\n",
    "        fc = tf.maximum(alpha * fc, fc)\n",
    "        #print(fc)\n",
    "        # 7x7x1024 now\n",
    "        \n",
    "        # Reshape it to start the convolutional stack\n",
    "        #x1 = tf.layers.batch_normalization(x1, training=is_train)\n",
    "        \n",
    "        \n",
    "        \n",
    "        # Deconv1\n",
    "        dconv1 = tf.layers.conv2d_transpose(fc, 512, 3, strides=2, padding='same')\n",
    "        dconv1 = tf.layers.batch_normalization(dconv1, training=is_train)\n",
    "        dconv1 = tf.maximum(alpha * dconv1, dconv1)\n",
    "        #print(dconv1)\n",
    "        # 14x14x512 now\n",
    "        \n",
    "        dconv2 = tf.layers.conv2d_transpose(dconv1, 256, 3, strides=2, padding='same')\n",
    "        dconv2 = tf.layers.batch_normalization(dconv2, training=is_train)\n",
    "        dconv2 = tf.maximum(alpha * dconv2, dconv2)\n",
    "        #print(dconv2)\n",
    "        # 28x28x256 now\n",
    "        \n",
    "        dconv3 = tf.layers.conv2d_transpose(dconv2, 128, 5, strides=1, padding='same')\n",
    "        dconv3 = tf.layers.batch_normalization(dconv3, training=is_train)\n",
    "        dconv3 = tf.maximum(alpha * dconv3, dconv3)\n",
    "        #print(dconv3)\n",
    "        # 28x28x128 now\n",
    "        \n",
    "        # Output layer\n",
    "        logits = tf.layers.conv2d_transpose(dconv3, out_channel_dim, 5, strides=1, padding='same')\n",
    "        #print(logits)\n",
    "        # 28x28x(channels) now\n",
    "        \n",
    "        out = tf.tanh(logits)\n",
    "    \n",
    "    return out\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(generator, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss\n",
    "Implement `model_loss` to build the GANs for training and calculate the loss.  The function should return a tuple of (discriminator loss, generator loss).  Use the following functions you implemented:\n",
    "- `discriminator(images, reuse=False)`\n",
    "- `generator(z, out_channel_dim, is_train=True)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def model_loss(input_real, input_z, out_channel_dim):\n",
    "    \"\"\"\n",
    "    Get the loss for the discriminator and generator\n",
    "    :param input_real: Images from the real dataset\n",
    "    :param input_z: Z input\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :return: A tuple of (discriminator loss, generator loss)\n",
    "    \"\"\"\n",
    "    g_model = generator(input_z, out_channel_dim, True)\n",
    "    d_model_real, d_logits_real = discriminator(input_real)\n",
    "    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)\n",
    "\n",
    "    d_loss_real = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)))\n",
    "    d_loss_fake = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))\n",
    "    g_loss = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))\n",
    "\n",
    "    d_loss = d_loss_real + d_loss_fake\n",
    "    \n",
    "    return d_loss, g_loss\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_loss(model_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization\n",
    "Implement `model_opt` to create the optimization operations for the GANs. Use [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) to get all the trainable variables.  Filter the variables with names that are in the discriminator and generator scope names.  The function should return a tuple of (discriminator training operation, generator training operation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
    "    \"\"\"\n",
    "    Get optimization operations\n",
    "    :param d_loss: Discriminator loss Tensor\n",
    "    :param g_loss: Generator loss Tensor\n",
    "    :param learning_rate: Learning Rate Placeholder\n",
    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :return: A tuple of (discriminator training operation, generator training operation)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Get weights and bias to update\n",
    "    t_vars = tf.trainable_variables()\n",
    "    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
    "    g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
    "\n",
    "    # Optimize\n",
    "    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
    "        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n",
    "        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n",
    "\n",
    "    \n",
    "    return d_train_opt, g_train_opt\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_opt(model_opt, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network Training\n",
    "### Show Output\n",
    "Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "\n",
    "def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):\n",
    "    \"\"\"\n",
    "    Show example output for the generator\n",
    "    :param sess: TensorFlow session\n",
    "    :param n_images: Number of Images to display\n",
    "    :param input_z: Input Z Tensor\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :param image_mode: The mode to use for images (\"RGB\" or \"L\")\n",
    "    \"\"\"\n",
    "    cmap = None if image_mode == 'RGB' else 'gray'\n",
    "    z_dim = input_z.get_shape().as_list()[-1]\n",
    "    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])\n",
    "\n",
    "    samples = sess.run(\n",
    "        generator(input_z, out_channel_dim, False),\n",
    "        feed_dict={input_z: example_z})\n",
    "\n",
    "    images_grid = helper.images_square_grid(samples, image_mode)\n",
    "    pyplot.imshow(images_grid, cmap=cmap)\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train\n",
    "Implement `train` to build and train the GANs.  Use the following functions you implemented:\n",
    "- `model_inputs(image_width, image_height, image_channels, z_dim)`\n",
    "- `model_loss(input_real, input_z, out_channel_dim)`\n",
    "- `model_opt(d_loss, g_loss, learning_rate, beta1)`\n",
    "\n",
    "Use the `show_generator_output` to show `generator` output while you train. Running `show_generator_output` for every batch will drastically increase training time and increase the size of the notebook.  It's recommended to print the `generator` output every 100 batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.core.debugger import Tracer\n",
    "\n",
    "def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):\n",
    "    \"\"\"\n",
    "    Train the GAN\n",
    "    :param epoch_count: Number of epochs\n",
    "    :param batch_size: Batch Size\n",
    "    :param z_dim: Z dimension\n",
    "    :param learning_rate: Learning Rate\n",
    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :param get_batches: Function to get batches\n",
    "    :param data_shape: Shape of the data\n",
    "    :param data_image_mode: The image mode to use for images (\"RGB\" or \"L\")\n",
    "    \"\"\"\n",
    "    \n",
    "    # create input placeholders\n",
    "    #print(data_shape)\n",
    "    image_shape = data_shape[1:]\n",
    "    #print(image_shape)\n",
    "    out_channel_dim = image_shape[-1]\n",
    "    #print(out_channel_dim)\n",
    "    \n",
    "    inputs_real, inputs_z, lr_rate = model_inputs(*image_shape, z_dim)\n",
    "    #print(inputs_real)\n",
    "    d_loss, g_loss = model_loss(inputs_real, inputs_z, out_channel_dim)\n",
    "    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr_rate, beta1)\n",
    "    #print(inputs_real)\n",
    "    \n",
    "    #t_vars = tf.trainable_variables()\n",
    "    #g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
    "    #saver = tf.train.Saver(var_list=g_vars)\n",
    "    \n",
    "    def scale(x, target_range=(-1,1)):\n",
    "        min, max = target_range\n",
    "        \n",
    "        # One liner, but too expensive \n",
    "        #x = (x - x.min()) * (max - min) / (x.max() - x.min()) + min\n",
    "        \n",
    "        # First scale to [0,1]\n",
    "        x = ((x - x.min())/(x.max() - x.min()))\n",
    "    \n",
    "        # Then scale to target_range\n",
    "        min, max = target_range\n",
    "        x = x * (max - min) + min\n",
    "        return x\n",
    "    \n",
    "    \n",
    "    steps = 0\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        for epoch_i in range(epoch_count):\n",
    "            for batch_images in get_batches(batch_size):\n",
    "                steps += 1\n",
    "                #print(\"Step {} ...\".format(steps))\n",
    "                \n",
    "                #1 Scale real images to [-1,1] range since that is also the generator output scale\n",
    "                batch_images = scale(batch_images, target_range=(-1,1))\n",
    "                \n",
    "                \n",
    "                #3 Sample random noise for generator\n",
    "                batch_z = np.random.uniform(-1,1, size=(batch_size, z_dim))\n",
    "                \n",
    "                \n",
    "                #3 Run optimizers\n",
    "                #print(batch_images.shape)\n",
    "                            \n",
    "                \n",
    "                # Optimize discriminator\n",
    "                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images,\n",
    "                                                     inputs_z: batch_z,\n",
    "                                                     lr_rate: learning_rate})\n",
    "                \n",
    "                \n",
    "                \n",
    "                \n",
    "                # Optimize generator (Run twice to avoid faster convergence of D)\n",
    "                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n",
    "                                                     inputs_real: batch_images,\n",
    "                                                     lr_rate: learning_rate})\n",
    "                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n",
    "                                                     inputs_real: batch_images,\n",
    "                                                     lr_rate: learning_rate})\n",
    "                \n",
    "                \n",
    "                #Tracer()() #this one triggers the debugger\n",
    "                if steps % print_every == 0:\n",
    "                    \n",
    "                    d_train_loss = d_loss.eval({inputs_real: batch_images,\n",
    "                                                inputs_z: batch_z,\n",
    "                                                lr_rate: learning_rate})\n",
    "                \n",
    "                    # Optimize generator\n",
    "                    g_train_loss = g_loss.eval({inputs_z: batch_z,\n",
    "                                                inputs_real: batch_images,\n",
    "                                                lr_rate: learning_rate})\n",
    "                    \n",
    "                    #Tracer()() #this one triggers the debugger\n",
    "                    print(\"Epoch {}/{}-Step {}...\".format(epoch_i+1, epoch_count,steps),\n",
    "                          \"Discriminator Loss: {:.4f}...\".format(d_train_loss),\n",
    "                          \"Generator Loss: {:.4f}\".format(g_train_loss))\n",
    "                    \n",
    "                if steps % show_every == 0:\n",
    "                        show_generator_output(sess, 9, inputs_z, out_channel_dim, data_image_mode)\n",
    "                \n",
    "                \n",
    "                \n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST\n",
    "Test your GANs architecture on MNIST.  After 2 epochs, the GANs should be able to generate images that look like handwritten digits.  Make sure the loss of the generator is lower than the loss of the discriminator or close to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 10... Discriminator Loss: 0.9267... Generator Loss: 3.0303\n",
      "Epoch 1/2-Step 20... Discriminator Loss: 0.0128... Generator Loss: 5.2754\n",
      "Epoch 1/2-Step 30... Discriminator Loss: 1.0608... Generator Loss: 2.6022\n",
      "Epoch 1/2-Step 40... Discriminator Loss: 1.6446... Generator Loss: 1.1610\n",
      "Epoch 1/2-Step 50... Discriminator Loss: 1.0090... Generator Loss: 1.2422\n",
      "Epoch 1/2-Step 60... Discriminator Loss: 1.1629... Generator Loss: 1.4662\n",
      "Epoch 1/2-Step 70... Discriminator Loss: 1.5492... Generator Loss: 1.4174\n",
      "Epoch 1/2-Step 80... Discriminator Loss: 1.3179... Generator Loss: 0.9317\n",
      "Epoch 1/2-Step 90... Discriminator Loss: 1.7052... Generator Loss: 1.2884\n",
      "Epoch 1/2-Step 100... Discriminator Loss: 2.0016... Generator Loss: 0.2221\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e886f6c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 110... Discriminator Loss: 1.0928... Generator Loss: 3.6356\n",
      "Epoch 1/2-Step 120... Discriminator Loss: 1.8290... Generator Loss: 0.5105\n",
      "Epoch 1/2-Step 130... Discriminator Loss: 1.8758... Generator Loss: 0.6536\n",
      "Epoch 1/2-Step 140... Discriminator Loss: 0.9872... Generator Loss: 1.5308\n",
      "Epoch 1/2-Step 150... Discriminator Loss: 0.8386... Generator Loss: 3.2762\n",
      "Epoch 1/2-Step 160... Discriminator Loss: 0.4610... Generator Loss: 1.8612\n",
      "Epoch 1/2-Step 170... Discriminator Loss: 0.8992... Generator Loss: 3.7001\n",
      "Epoch 1/2-Step 180... Discriminator Loss: 0.3389... Generator Loss: 3.2347\n",
      "Epoch 1/2-Step 190... Discriminator Loss: 1.2121... Generator Loss: 0.9055\n",
      "Epoch 1/2-Step 200... Discriminator Loss: 3.2045... Generator Loss: 0.0909\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n003KPb2fisJv983YU1a
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7b097470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 210... Discriminator Loss: 1.3834... Generator Loss: 3.7705\n",
      "Epoch 1/2-Step 220... Discriminator Loss: 0.8444... Generator Loss: 1.6745\n",
      "Epoch 1/2-Step 230... Discriminator Loss: 0.3921... Generator Loss: 1.8625\n",
      "Epoch 1/2-Step 240... Discriminator Loss: 0.6757... Generator Loss: 1.7159\n",
      "Epoch 1/2-Step 250... Discriminator Loss: 0.8184... Generator Loss: 1.3769\n",
      "Epoch 1/2-Step 260... Discriminator Loss: 0.3848... Generator Loss: 2.3387\n",
      "Epoch 1/2-Step 270... Discriminator Loss: 0.4555... Generator Loss: 2.0942\n",
      "Epoch 1/2-Step 280... Discriminator Loss: 2.1406... Generator Loss: 0.5748\n",
      "Epoch 1/2-Step 290... Discriminator Loss: 0.2285... Generator Loss: 2.4981\n",
      "Epoch 1/2-Step 300... Discriminator Loss: 1.9429... Generator Loss: 0.4845\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmqDTVlBR1Vh/C6UsQVA
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8d36d2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 310... Discriminator Loss: 1.4466... Generator Loss: 1.9327\n",
      "Epoch 1/2-Step 320... Discriminator Loss: 0.9744... Generator Loss: 0.8699\n",
      "Epoch 1/2-Step 330... Discriminator Loss: 1.4074... Generator Loss: 1.3701\n",
      "Epoch 1/2-Step 340... Discriminator Loss: 1.0074... Generator Loss: 1.0729\n",
      "Epoch 1/2-Step 350... Discriminator Loss: 0.4718... Generator Loss: 2.2176\n",
      "Epoch 1/2-Step 360... Discriminator Loss: 1.0672... Generator Loss: 0.8503\n",
      "Epoch 1/2-Step 370... Discriminator Loss: 1.8639... Generator Loss: 0.4736\n",
      "Epoch 1/2-Step 380... Discriminator Loss: 1.0121... Generator Loss: 0.9073\n",
      "Epoch 1/2-Step 390... Discriminator Loss: 0.3627... Generator Loss: 4.4332\n",
      "Epoch 1/2-Step 400... Discriminator Loss: 2.8513... Generator Loss: 0.1151\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nHXRQkK222mqhrTPzCDL
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e92c2f9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 410... Discriminator Loss: 1.6384... Generator Loss: 0.4555\n",
      "Epoch 1/2-Step 420... Discriminator Loss: 1.0845... Generator Loss: 0.9582\n",
      "Epoch 1/2-Step 430... Discriminator Loss: 2.6283... Generator Loss: 0.2061\n",
      "Epoch 1/2-Step 440... Discriminator Loss: 2.0905... Generator Loss: 0.2952\n",
      "Epoch 1/2-Step 450... Discriminator Loss: 3.3657... Generator Loss: 0.1126\n",
      "Epoch 1/2-Step 460... Discriminator Loss: 0.8958... Generator Loss: 0.8718\n",
      "Epoch 1/2-Step 470... Discriminator Loss: 3.1746... Generator Loss: 0.0618\n",
      "Epoch 1/2-Step 480... Discriminator Loss: 1.5734... Generator Loss: 0.5139\n",
      "Epoch 1/2-Step 490... Discriminator Loss: 0.7358... Generator Loss: 2.6876\n",
      "Epoch 1/2-Step 500... Discriminator Loss: 1.1104... Generator Loss: 2.3262\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e81125048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 510... Discriminator Loss: 1.3891... Generator Loss: 0.5434\n",
      "Epoch 1/2-Step 520... Discriminator Loss: 1.4872... Generator Loss: 0.8000\n",
      "Epoch 1/2-Step 530... Discriminator Loss: 0.7497... Generator Loss: 1.9807\n",
      "Epoch 1/2-Step 540... Discriminator Loss: 1.5252... Generator Loss: 1.0185\n",
      "Epoch 1/2-Step 550... Discriminator Loss: 1.4869... Generator Loss: 0.4923\n",
      "Epoch 1/2-Step 560... Discriminator Loss: 0.9228... Generator Loss: 2.2592\n",
      "Epoch 1/2-Step 570... Discriminator Loss: 0.8381... Generator Loss: 1.1396\n",
      "Epoch 1/2-Step 580... Discriminator Loss: 0.5408... Generator Loss: 1.6971\n",
      "Epoch 1/2-Step 590... Discriminator Loss: 3.1695... Generator Loss: 4.3885\n",
      "Epoch 1/2-Step 600... Discriminator Loss: 1.1400... Generator Loss: 1.2354\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfhX6tAFU0J9Fos7feee
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79abfd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 610... Discriminator Loss: 1.2808... Generator Loss: 0.7663\n",
      "Epoch 1/2-Step 620... Discriminator Loss: 1.1544... Generator Loss: 1.6539\n",
      "Epoch 1/2-Step 630... Discriminator Loss: 2.0728... Generator Loss: 0.2722\n",
      "Epoch 1/2-Step 640... Discriminator Loss: 2.4404... Generator Loss: 2.0143\n",
      "Epoch 1/2-Step 650... Discriminator Loss: 1.0015... Generator Loss: 1.1314\n",
      "Epoch 1/2-Step 660... Discriminator Loss: 2.2646... Generator Loss: 0.2215\n",
      "Epoch 1/2-Step 670... Discriminator Loss: 0.8644... Generator Loss: 1.3619\n",
      "Epoch 1/2-Step 680... Discriminator Loss: 2.0751... Generator Loss: 0.4561\n",
      "Epoch 1/2-Step 690... Discriminator Loss: 1.0712... Generator Loss: 1.1022\n",
      "Epoch 1/2-Step 700... Discriminator Loss: 0.8807... Generator Loss: 3.1525\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztvXnYFMW1+P8pEKNxQXFBFBLUoMYlkqj8RIzBBcVd7zWu\nMV41MXrVH5pEEbegV40mucZczTUSt7gbXHEXics1RgVUQCEqGBeQRSMKxAWQ+v4xc2pO89b7Ts9M\nT78zzPk8j4/FmXe6q6u7p06dOovz3mMYRmvRpbM7YBhG/tiLbxgtiL34htGC2ItvGC2IvfiG0YLY\ni28YLYi9+IbRgtT04jvnhjrnXnfOTXfOnZVVpwzDqC+uWgce51xX4A1gCDATGA8c4b2fml33DMOo\nByvV8N0BwHTv/VsAzrk7gAOBdl/8rl27+m7dugGwdOlSAPQPj7SbzZvQOReVd+nSpc3n0pbPAL7y\nla+E9hdffAGUxgfi49JsY6SRMdDjoscjJltllVWA0vjAijdGsXGJjVHsc3mGPvvsMxYvXhx/IBW1\nvPgbAe+pf88E/r+OvtCtWzf69OkDwD//+U8geSOXLFkCwLJly4KskX8Y5EZ07do1yFZaqTSkcjNW\nXnnlNp+vuuqqQdavX7/Qnj59OgAfffRRkMkYLV68OMhiY9SID33sh07GS4+LvNhQGqOvfvWrQSZj\nNGPGjCCTZwhKYxMbo0Z5hmIThH52ZFLUMj0pSFs/YzJum266KQB//etfU/Wllhc/Fc65E4ATINlh\nwzA6j1rexFlAH/Xv3kVZAu/9KGAUFFT9999/H4DPP/9cPtd/W0N38kdmFD37itYCBbUL4qqZnu3k\nlx5g9uzZQGl8oDQu+jzNgvT9yy+/DDJp69l50aJFod3RGMnzA/ExauRnKNY3vVzRbeFf//pXG5nW\nnmSMZFLVY9oRtVj1xwP9nHMbO+dWBg4HxtRwPMMwcqLqGd97v9Q5dwrwGNAVuN57/1qZ74QZsZF/\nmbMmdq16BoxpDM2sCdVKR1qC1qhaYYxi1xV7XmSmTzsONS26vfcPAw/XcgzDMPLHPPcMowXJ3cy+\n/F5lo6hosf1SbUTpyHikVdJyxAx12kgV26ttRqNeFuixFpVW3xM9Rnk/R7Gtubz6oM8jz4Y8Q2mf\nFZvxDaMFyXXG9963capolBlf90NmcD2Ty0yTlUFJH3vhwoWhHdsibFX0GMgYNYpBrzO1DY2MkWyH\n2oxvGEa72ItvGC1I7sa9mP90MyA+0TFvsVrRBitT8Uvo8RX/9c4cn/XWWy+0d91119B+7LHHAPjk\nk09y75Og/fvTYDO+YbQg9uIbRgvSaeFyjbaPH2P99dcP7cMPPxyA2267Lcg+/PDDTM6jAytiMdl5\nj5Feeqy55pqhLWGyWt0++OCDATjzzDODbLXVVgvtb37zm0AyhLYaZIzaWxbVc4y22GILAF588cUg\n0wFEDz74IADDhg0Lsrlz5wLxwJt6UKkrvM34htGCVJ16q6qTOecbdabXM8m6664LwLnnnhtkkgji\ntNNOC7I333wTqN3gpJMtxJKR5MXaa68NwK9+9asg23nnnUN7gw02AJIzuhiV2stCJMyZMye0Dzro\nIABefvnlINPBNzEkcUm5ZCS1ItcxePDgIBszphB0qhODaD+MmTNnAsmkMj179gSSz5VoAQBHHXUU\nABMmTMik3zpD0bJly8pm4LEZ3zBaEHvxDaMFyV3Vz+1ky7HllluG9vDhwwFYY401gqx3796hvfHG\nGwOw+uqrB5lkQrnpppuC7M477wSSKcW0Eevtt98Gknv/MTpjH19UWlHfAR555BEANt988yDTRqy0\ngSnl1H75zh//+McgO+mkkzo8piwpKgmI6gjdR/0cSJ/233//INP5AGN9lHYsYWglSD7BbbbZJsjK\nPTuCHh/vvan6hmG0ZYWc8cWwAvDEE08AsNlmmwWZ/DrqrZaYcUkbc2SG0DOyzO5PPfVUkN1xxx2h\n/eijjwKl3HvtkdeMv9FGG4X2GWecAcDxxx8fZLEsrhrp29/+9rcgky2uAw44IMgk42t7yDM3evTo\nIDviiCPafK6RMapmfHROw169egFw3nnnBdmRRx4Z2jr78fLEwmGh4y3Y9lJld4Q+9u677x7aTz/9\ndLvf0eOTyYzvnLveOTfPOfeqkvVwzo11zr1Z/P/a5Y5jGEbjkEbVvxEYupzsLGCc974fMK74b8Mw\nmoSynnve+2ecc32XEx8IDC62/wQ8BQzPsF8VI3vQAPfdd19o9+3bF0juI48fPx6An//850H23nul\n2iCi6urPL7zwQiCprolXmzb+TJkyJbTTpjqu53JLfBIALrnkktA+8MADgaQaLIYzvR8tHosADz30\nUJvji4p5//33B5lW4eW+zJpVyrx+9tlnt/lOuTGoZYy6d+8e2qI677TTTkGmDZhyHj0GEoRzzDHH\nBJlevpXzQRD0WMtyVC+bZCmml37aACpG1yyel2qNez2997OL7TlAz47+2DCMxqJmX33vve/IaKcr\n6RiG0RhU++LPdc718t7Pds71Aua194e6kk49rPpSi+/aa68NMl2L7t133wVgwIABQRarTqIRlffi\niy8OsrvvvhtIWlZFjd1+++2DTO+L/+Mf/0h5Fdkjy5Unn3wyyGTZAyV18YMPPggyCbS5/fbbU59H\ndj622mqrIPvZz34W2rKzkVVAU6306NEDSPpo6N0dianfdtttg0y72taCXhKIm688vwDz588HkksT\nnQMgthtVLdWq+mMAWfAcA9zfwd8ahtFglJ3xnXO3UzDkreucmwn8ArgU+LNz7njgHeDQenYy0qfQ\nfu6554DkTKt/WX/3u98B5Wf5cvz9738HYJdddgmy114rFA7SfgP/+Z//GdoSgJGVt1k59Lh897vf\nBZLajzYaiQ+C3j9/9tlnU51HX+9VV10FJD3MfvnLX7Y5T63UYtDShjoJtdbHW7BgQWifcEJhVZrV\nLF8JUhdQz/iiGWRNGqv+Ee18tHs7csMwGhxz2TWMFqQpC9brzDiidmr1/rrrrgttvQ+aBW+99VYb\nmVaht95669AW98+0gRbVIiq+3ieWpY9eZnz66aehLXvokydP7vDY3/jGN0L7D3/4AwCDBg0KMjGG\nPfxwqYSiGKmgMfIuaBdiyRi0zjrrBNnHH38c2mPHjs2vYySXZ1/72teA5JhNnDix4uOkwWZ8w2hB\nmmrGl1+1ww47LMhkttUGnKuvvjq0s55xNtxwwzb90bOqhOpCcoatJ3KNWusRQ51O+ay3ho499lgg\nadyTzDrlwkv1mEpgjzae6hl29uzZdBaypXnFFVcEmYRcx2olQml77Y033siji8GLEeI5DbXG2pGR\nuNLn3GZ8w2hB7MU3jBakqVR9QQfCiIov++yQDLjJCgnk0EYsUcl08MpvfvOb0M47SEcfR/Z///zn\nPwfZiSeeGNriBaazz5Q7poy1Vo1FndZ+FI2CpMXeYYcdgixWcUYHWf3pT38CYODAgXXtm/Tj/PPP\nb/PZ888/H9o6GWeWy1ab8Q2jBbEX3zBakKZS9UXVeeWVV4LshRdeAErus5CMzRdrcyUpm0SV1Xu9\nDzzwAJDc15bAE20Z17sLnYmM1emnnx5kklMASoFFWm2XsdT78BpZ7ujxl5Rm3/rWt4JMj39WdRSq\nOc4+++wDxJNlxo4NpWAunaqtHhb+K6+8Ekj6XkiM/9577x1kaZ8ns+obhlGWpprxBT0j7bnnnkBy\nf11nnfnoo48AWLRoUYfH1DOfZFrRM6QcU+9Li9GoXDLNzkTPBDIWAI8//njFxxJj5fXXXx9kl112\nGZBMT65DoEVT6gwkTXUlXm0yXjpDTz1m/O9///uJ80HJI7Lcs5oFNuMbRgtiL75htCBNqeprJBuJ\nZNqBpOotGVd0oIy4Puo9XV1pR1R8bdyTfXHtLqyz17QSep9Z0IU/dYaYziySmrayjTb8yrMzbdq0\nDr9TTRlznQFJ/Ce0S/W
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8111a0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 710... Discriminator Loss: 1.4505... Generator Loss: 1.3298\n",
      "Epoch 1/2-Step 720... Discriminator Loss: 1.6401... Generator Loss: 1.3325\n",
      "Epoch 1/2-Step 730... Discriminator Loss: 2.1060... Generator Loss: 3.4476\n",
      "Epoch 1/2-Step 740... Discriminator Loss: 1.3121... Generator Loss: 0.8670\n",
      "Epoch 1/2-Step 750... Discriminator Loss: 1.2358... Generator Loss: 2.5686\n",
      "Epoch 1/2-Step 760... Discriminator Loss: 2.6190... Generator Loss: 0.1099\n",
      "Epoch 1/2-Step 770... Discriminator Loss: 1.5482... Generator Loss: 2.8853\n",
      "Epoch 1/2-Step 780... Discriminator Loss: 2.5531... Generator Loss: 0.1868\n",
      "Epoch 1/2-Step 790... Discriminator Loss: 1.1788... Generator Loss: 0.8387\n",
      "Epoch 1/2-Step 800... Discriminator Loss: 0.6670... Generator Loss: 1.3919\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMYhCfAtPB+uMHTu29h0
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e796fe320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 810... Discriminator Loss: 1.8112... Generator Loss: 0.5763\n",
      "Epoch 1/2-Step 820... Discriminator Loss: 1.4400... Generator Loss: 0.8454\n",
      "Epoch 1/2-Step 830... Discriminator Loss: 2.1204... Generator Loss: 0.1975\n",
      "Epoch 1/2-Step 840... Discriminator Loss: 0.9309... Generator Loss: 0.8856\n",
      "Epoch 1/2-Step 850... Discriminator Loss: 2.2666... Generator Loss: 0.2093\n",
      "Epoch 1/2-Step 860... Discriminator Loss: 2.7204... Generator Loss: 0.2036\n",
      "Epoch 1/2-Step 870... Discriminator Loss: 1.4407... Generator Loss: 0.7119\n",
      "Epoch 1/2-Step 880... Discriminator Loss: 1.8585... Generator Loss: 0.3937\n",
      "Epoch 1/2-Step 890... Discriminator Loss: 1.1191... Generator Loss: 1.4012\n",
      "Epoch 1/2-Step 900... Discriminator Loss: 1.9723... Generator Loss: 2.9803\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSJhNxxJqGEb9kdgt1zm
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79525cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 910... Discriminator Loss: 1.3118... Generator Loss: 2.4486\n",
      "Epoch 1/2-Step 920... Discriminator Loss: 1.2882... Generator Loss: 0.5410\n",
      "Epoch 1/2-Step 930... Discriminator Loss: 3.1985... Generator Loss: 0.1271\n",
      "Epoch 1/2-Step 940... Discriminator Loss: 2.2658... Generator Loss: 0.2157\n",
      "Epoch 1/2-Step 950... Discriminator Loss: 1.4683... Generator Loss: 0.6122\n",
      "Epoch 1/2-Step 960... Discriminator Loss: 1.3805... Generator Loss: 0.5894\n",
      "Epoch 1/2-Step 970... Discriminator Loss: 0.9084... Generator Loss: 1.3491\n",
      "Epoch 1/2-Step 980... Discriminator Loss: 1.1829... Generator Loss: 0.8846\n",
      "Epoch 1/2-Step 990... Discriminator Loss: 2.6936... Generator Loss: 0.1252\n",
      "Epoch 1/2-Step 1000... Discriminator Loss: 1.0920... Generator Loss: 3.3635\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVRgrTw2ZKkK6HLRkM9J
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e791edcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1010... Discriminator Loss: 0.4717... Generator Loss: 2.0444\n",
      "Epoch 1/2-Step 1020... Discriminator Loss: 2.2474... Generator Loss: 3.6244\n",
      "Epoch 1/2-Step 1030... Discriminator Loss: 1.1826... Generator Loss: 1.1267\n",
      "Epoch 1/2-Step 1040... Discriminator Loss: 0.8570... Generator Loss: 1.7126\n",
      "Epoch 1/2-Step 1050... Discriminator Loss: 1.2136... Generator Loss: 0.6905\n",
      "Epoch 1/2-Step 1060... Discriminator Loss: 1.0117... Generator Loss: 0.8767\n",
      "Epoch 1/2-Step 1070... Discriminator Loss: 1.6421... Generator Loss: 0.4355\n",
      "Epoch 1/2-Step 1080... Discriminator Loss: 3.1265... Generator Loss: 0.1189\n",
      "Epoch 1/2-Step 1090... Discriminator Loss: 1.5463... Generator Loss: 0.5217\n",
      "Epoch 1/2-Step 1100... Discriminator Loss: 1.9496... Generator Loss: 0.3557\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuusC8ZTnkrkJitO5d4Q
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79666710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1110... Discriminator Loss: 1.0777... Generator Loss: 0.8982\n",
      "Epoch 1/2-Step 1120... Discriminator Loss: 0.8473... Generator Loss: 2.6787\n",
      "Epoch 1/2-Step 1130... Discriminator Loss: 2.1421... Generator Loss: 0.4104\n",
      "Epoch 1/2-Step 1140... Discriminator Loss: 0.5819... Generator Loss: 1.3670\n",
      "Epoch 1/2-Step 1150... Discriminator Loss: 0.4012... Generator Loss: 3.8247\n",
      "Epoch 1/2-Step 1160... Discriminator Loss: 1.8693... Generator Loss: 0.3963\n",
      "Epoch 1/2-Step 1170... Discriminator Loss: 1.1938... Generator Loss: 0.7962\n",
      "Epoch 1/2-Step 1180... Discriminator Loss: 0.6501... Generator Loss: 1.2007\n",
      "Epoch 1/2-Step 1190... Discriminator Loss: 2.0899... Generator Loss: 0.3304\n",
      "Epoch 1/2-Step 1200... Discriminator Loss: 1.8512... Generator Loss: 0.6061\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH2TXX399aNdyNZt8efH
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7928ef60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1210... Discriminator Loss: 2.4600... Generator Loss: 0.2065\n",
      "Epoch 1/2-Step 1220... Discriminator Loss: 1.1053... Generator Loss: 1.4613\n",
      "Epoch 1/2-Step 1230... Discriminator Loss: 3.1921... Generator Loss: 0.1285\n",
      "Epoch 1/2-Step 1240... Discriminator Loss: 2.4057... Generator Loss: 0.2731\n",
      "Epoch 1/2-Step 1250... Discriminator Loss: 0.9394... Generator Loss: 2.9006\n",
      "Epoch 1/2-Step 1260... Discriminator Loss: 0.8145... Generator Loss: 1.2383\n",
      "Epoch 1/2-Step 1270... Discriminator Loss: 0.3363... Generator Loss: 1.9907\n",
      "Epoch 1/2-Step 1280... Discriminator Loss: 2.1134... Generator Loss: 0.2527\n",
      "Epoch 1/2-Step 1290... Discriminator Loss: 4.0813... Generator Loss: 0.0666\n",
      "Epoch 1/2-Step 1300... Discriminator Loss: 0.5599... Generator Loss: 2.2968\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXe4lMXVwH8DWIgSATWCIGJir6iggMaGIlGjabYkaIgl\n8Uuxxl7QiDFGTTSJKHYNFlSwFwgRNcaGXRFELAEERSIGjQ2Y74/dM3vey+zdd+/2u+f3PDzMPdvm\nnbfMmTOnOO89hmE0Fx1q3QHDMKqP3fiG0YTYjW8YTYjd+IbRhNiNbxhNiN34htGE2I1vGE1ISTe+\nc26Yc26Gc+4N59zJ5eqUYRiVxbXVgcc51xF4HdgDmAM8AxzsvZ9Wvu4ZhlEJOpXw2e2AN7z3bwI4\n524B9gPy3vidOnXyK6ywAgBLlixZ7nV5CC1btmw5WT3jnEv9eseOHQHo0CGnbMmYAHz55ZcALF26\ndLnvyTcu9TZG+nilrWWx/hYao06dMpeqjA8kx0i+MzYu9TY+Gn3ccrz5+ivv1eMiYyXj89lnn/Hl\nl1+2fkFS2o3fC5it/p4DbN/aB1ZYYQX69u0LwIcffggkL+Yvvvgi8T8kT7S8t5YXfewClf/zvVff\n2KuuuioAXbp0CbKePXuG9rx584Dc+EDuGPONi7TL9cAs5kEWe6+WrbzyykByDOShr/uox/CrX/0q\nAF/5yleCrEePHkBufAD+85//hLY8BD7//PPlfkdPMq09IIqh0BgV+oy0V1xxxSCTduyhD7mbW187\nq6yyCpAbn6lTp6bqSyk3fiqcc0cCR0Ku44Zh1JZS7sS5wDrq795ZWQLv/RhgDGRU/fnz5wPw6aef\nyuvhvTJj6ZlLt+sB3V95Mud7Qgt6ppa2HD8kn/oyPp999ln0N1v+NlR2jGKqaKHZTvdHZls9BrEZ\nX3+nvLdz585BJmMk4wPxMYpdT4XOT6m0Nh75NAvpW0xzi71P/47Waj755BMgN6nGltAxSrHqPwNs\n4Jxbzzm3InAQcHcJ32cYRpVo84zvvV/inPsl8BDQEbjGe/9qa59ZtmxZeFrJUzj2dKtnY4wmbT/1\n+2JP9YULF4Z2y/Ep9bfbgqzLATbbbLPQHjx4MACzZs0KspdeegmABQsWBJmedWRWLqa/8hk9G4qN\nQGsOhQygldSEChkoY0bNWH/TztD6N2N2jA8++KCo7ytp0e29vx+4v5TvMAyj+pjnnmE0IVU1szvn\nEnuQ0Hh79qUix6jVvrRbltUaH729OGHChNCWrcizzjoryCZPngwkDW2lElPRY99fL8vE2PkRFb8S\n/WltfNL+ns34htGEVHXG994vtwXWDLO8JuadGDNY1XIb87TTTgvttddeO7TfffddAEaPHh1kWlsp\nN7Exyme8q+R1JFrqWmutFWTbbbddaM+dm9nFfuWVV4KsNQ/MciJjIONjM75hGHmxG98wmpCqq/rF\n7Fu2Z/J5Z9VSxRd/+R//+MdBpvehTz/9dKCy6n0+RGWO+d2XE/EQHDhwYJDdfvvtAKy++upBFvPW\n096YZ599NgAXXXRRkFVS7S/WuGkzvmE0IXbjG0YTUvVwuWaz4qehXpY/G2ywAZCMotRLD1F5a0FM\nlS0U45+WPfbYI7THjx8PJEOCY3vyerkjLrT6PO6www4A3HzzzUEm1n8o/5Ku2GWEzfiG0YRUfcav\np0AcPWPoRBGyX9u/f/8gE8PXCy+8EGQrrbQSALNn5/KRfPzxx6Gd9qleC2NZDPH+0jPXk08+GdoS\nAloLYn4fpVxDOhDp3HPPDW3Rdv73v/8F2UEHHQTAQw89FGR6jOTaOOSQQ4Jsn332AWDQoEFBVkmN\nqVi/GJvxDaMJsRvfMJqQujTu6UAeUacBvvnNbwKw2mqrBdnMmTMBWLx4cZDp19dYYw0AjjnmmCDb\neOONAejatWuQ6ZxvogbGUoVp9V1yvumgleuvvz609b5ua9RLlqHu3bsDyfG/8MILa9WdBOUaI1ne\nffe73w0y7ZY8Z84cAIYPHx5kerkTQ9Ts6667Lsgee+wxILkkqOR5Lva7bcY3jCakobJfnnLKKUDS\nq0rnq4sRC4qRDLZ/+9vfgmzcuHGhLQa6AQMGBJlsz+itHzGG/etf/1pO1oh89NFHQHKsCs12jYZs\n062//vpBpo/3nHPOAXKZhYpBa7NvvfXWcrJ6ouCM75y7xjn3vnPuFSXr7pyb5Jybmf2/W2W7aRhG\nOUmj6l8HDGshOxmY7L3fAJic/dswjAYhVQkt51xf4F7v/ebZv2cAu3jv5znnegJTvPcbpfieovUe\nvdf+wx/+EEiq4Ntuuy2Q9Ir673//G9r3359JCfjcc88F2XvvvQck4+ALjYNkn5kxY0aQSdCGeLxB\nck+/0VhnnUy2dDGYQjIwpZb7+KWgr6H99tsPgL/+9a9BJokqAXbffXcgmTy0Lb8jRml9jcUMcNqQ\nWi7jn/e+YLWPthr31vLeS0mT+cBarb3ZMIz6omTjnvfetzaT60o6hmHUB2298d9zzvVUqv77+d6o\nK+m0RdXXKvjYsWMT/1cTseZ365azYy5atAjIpaRqdGSJpHcm6sWduC2IGq3V6d69ewO5mnOQXAbq\nenzFov0+pK1z4LfWR6huUtW2qvp3A4dm24cCd5WnO4ZhVIOCM75z7mZgF2AN59wc4CzgfGCcc+4w\n4B3ggEp2slZI1VaAq666CoCJEycG2U9+8hOg8gkVq4XMUi1ToDcqYmzTRjPxudD79OLJCXD44YcD\ncb8OXctvzz33DG25DrQn6PTp0wE488wzg0wboIVahWQXvPG99wfneWlImftiGEaVaB+PdsMwiiLV\nPn7ZfqwNxr1aICri3nvvHWRdunQB4JZbbgmyenXHbCuyZ//6668HWa9evUK7kd2RBQnG+ta3vhVk\nN910U2iLS2/M6KbRMjHgxYp86r39kSNHhrYEP1UicKeS+/iGYTQwNuNn0U/4o48+GoB99903yKSt\nw3/bGxLC/NprrwXZuuuuG9o6K017QmfjEUPfrrvuGmQS1DVlypQg096hYtzVWZy22GILIJdmG2DL\nLbcMbfGCPPHEE4NMNI9S70mb8Q3DiGI3vmE0IQ2v6sves1bXtEqa1niiPbleffVVAHr06BFkO+64\nIwBTp05te2frHDF8PfPMM0G22267hbZ4KhptQ/sLiB+B3seXTECljrOp+oZhRLEb3zCakIZKvSVo\n6+kdd9wBJJNCPvroo0V/p44133777QF48MEHg0xi1XUN9Pawr62RPWe9d7/VVluF9iOPPFL1PrUn\nxI0X4IknngByKd0ArrzySiCZnz9twtZisRnfMJqQhjLuyV77iBEjgkxm+p133jnI2pIoUSMzn1RQ\nATj11FMBuOSSS4JM0inXS+27UpHx1WHGeiyHDh1a9T61d3RI8Oabbw7ADTfcEGQSNFQIXd/PjHuG\nYUSxG98wmpCGUvU33HBDIBkTL3vPm222WZBpd0qhUFJDHUwh33XXXbn8In369AHg2WefDTJZCrz9\n9tvpD6IB0MsZXXFGctHrBJJGaUgSV8gl/ZT6BgA9e/YM7dZ8UkzVNwyjIHW5nZevdt6BBx4IJJ+S\nMisXCiDRmo18v86Ycthhh4W2VFPR24YSiKHz/emS2O2J008/PbQPPjiXh+W4444D4Pzzz696n8qN\nvsa0tifErpdKGHF1RR+53iQ0GJIzfiyDT1tJU0lnHefcw865ac65V51zR2flVk3HMBqUNKr+EuB4\n7/2mwEDgF865TbFqOobRsBRt3HPO3QX8JfuvqGo6zjkvalNrqYS1iq2DZy6++GIgFzADOfXrxhtv\nDLLHH388tGUJoOPKZW908ODBQaaLb4pH3qGHHhpk9957L1CZjCla1ayHrD46TfTChQtDW5Zdugx5\nofTR5UIbr8rxPXq5qK8DMSC//PLLQSbnXFfc0dWG2pKCXPqhA3JExZ83b16QnXDCCaF99913A/Hq\nPGLkXrp0aSrjXlFr/Gw
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7928e860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1310... Discriminator Loss: 2.8760... Generator Loss: 0.2637\n",
      "Epoch 1/2-Step 1320... Discriminator Loss: 1.3657... Generator Loss: 1.6037\n",
      "Epoch 1/2-Step 1330... Discriminator Loss: 1.3713... Generator Loss: 0.7570\n",
      "Epoch 1/2-Step 1340... Discriminator Loss: 2.7794... Generator Loss: 0.2296\n",
      "Epoch 1/2-Step 1350... Discriminator Loss: 1.6856... Generator Loss: 0.4993\n",
      "Epoch 1/2-Step 1360... Discriminator Loss: 0.5796... Generator Loss: 1.5256\n",
      "Epoch 1/2-Step 1370... Discriminator Loss: 1.6392... Generator Loss: 1.6606\n",
      "Epoch 1/2-Step 1380... Discriminator Loss: 0.8350... Generator Loss: 1.6210\n",
      "Epoch 1/2-Step 1390... Discriminator Loss: 2.5282... Generator Loss: 0.2095\n",
      "Epoch 1/2-Step 1400... Discriminator Loss: 1.0859... Generator Loss: 1.1357\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ne+/Qlnx02rurkV5rsbx
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79546710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1410... Discriminator Loss: 1.1798... Generator Loss: 0.9080\n",
      "Epoch 1/2-Step 1420... Discriminator Loss: 1.3249... Generator Loss: 0.7105\n",
      "Epoch 1/2-Step 1430... Discriminator Loss: 0.8106... Generator Loss: 1.0409\n",
      "Epoch 1/2-Step 1440... Discriminator Loss: 3.8837... Generator Loss: 0.0626\n",
      "Epoch 1/2-Step 1450... Discriminator Loss: 0.9882... Generator Loss: 0.8990\n",
      "Epoch 1/2-Step 1460... Discriminator Loss: 2.3555... Generator Loss: 0.2031\n",
      "Epoch 1/2-Step 1470... Discriminator Loss: 1.1362... Generator Loss: 0.7481\n",
      "Epoch 1/2-Step 1480... Discriminator Loss: 3.1776... Generator Loss: 0.1075\n",
      "Epoch 1/2-Step 1490... Discriminator Loss: 1.3520... Generator Loss: 1.5787\n",
      "Epoch 1/2-Step 1500... Discriminator Loss: 1.0190... Generator Loss: 1.8918\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ns35I3oWkfgoVvePnS2k
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e797f5da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1510... Discriminator Loss: 1.3130... Generator Loss: 3.2561\n",
      "Epoch 1/2-Step 1520... Discriminator Loss: 1.3796... Generator Loss: 0.5629\n",
      "Epoch 1/2-Step 1530... Discriminator Loss: 1.6151... Generator Loss: 0.6132\n",
      "Epoch 1/2-Step 1540... Discriminator Loss: 1.0203... Generator Loss: 1.5598\n",
      "Epoch 1/2-Step 1550... Discriminator Loss: 0.7105... Generator Loss: 1.3498\n",
      "Epoch 1/2-Step 1560... Discriminator Loss: 1.7961... Generator Loss: 0.4888\n",
      "Epoch 1/2-Step 1570... Discriminator Loss: 1.2594... Generator Loss: 2.2816\n",
      "Epoch 1/2-Step 1580... Discriminator Loss: 1.1150... Generator Loss: 0.7396\n",
      "Epoch 1/2-Step 1590... Discriminator Loss: 1.3450... Generator Loss: 0.7354\n",
      "Epoch 1/2-Step 1600... Discriminator Loss: 1.4369... Generator Loss: 0.6458\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZGzf5513XmiXM3iJIRO
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7913c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1610... Discriminator Loss: 1.7995... Generator Loss: 0.3567\n",
      "Epoch 1/2-Step 1620... Discriminator Loss: 1.0228... Generator Loss: 0.8237\n",
      "Epoch 1/2-Step 1630... Discriminator Loss: 2.8634... Generator Loss: 0.1942\n",
      "Epoch 1/2-Step 1640... Discriminator Loss: 2.6316... Generator Loss: 0.2240\n",
      "Epoch 1/2-Step 1650... Discriminator Loss: 1.7795... Generator Loss: 4.5748\n",
      "Epoch 1/2-Step 1660... Discriminator Loss: 1.5611... Generator Loss: 0.6124\n",
      "Epoch 1/2-Step 1670... Discriminator Loss: 0.7054... Generator Loss: 1.4339\n",
      "Epoch 1/2-Step 1680... Discriminator Loss: 2.1514... Generator Loss: 0.3458\n",
      "Epoch 1/2-Step 1690... Discriminator Loss: 1.7364... Generator Loss: 0.4061\n",
      "Epoch 1/2-Step 1700... Discriminator Loss: 3.0349... Generator Loss: 0.1355\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndVCL9C22rCh13yBTuhk
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8935d518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1710... Discriminator Loss: 0.9496... Generator Loss: 0.8696\n",
      "Epoch 1/2-Step 1720... Discriminator Loss: 1.4395... Generator Loss: 0.6739\n",
      "Epoch 1/2-Step 1730... Discriminator Loss: 0.6664... Generator Loss: 1.6997\n",
      "Epoch 1/2-Step 1740... Discriminator Loss: 2.6954... Generator Loss: 0.2849\n",
      "Epoch 1/2-Step 1750... Discriminator Loss: 1.3603... Generator Loss: 0.9009\n",
      "Epoch 1/2-Step 1760... Discriminator Loss: 1.0816... Generator Loss: 0.9107\n",
      "Epoch 1/2-Step 1770... Discriminator Loss: 0.8256... Generator Loss: 1.0552\n",
      "Epoch 1/2-Step 1780... Discriminator Loss: 0.4205... Generator Loss: 1.9499\n",
      "Epoch 1/2-Step 1790... Discriminator Loss: 1.6130... Generator Loss: 0.3562\n",
      "Epoch 1/2-Step 1800... Discriminator Loss: 2.5861... Generator Loss: 0.1800\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXhtkOtxWzl1rVHKP1tI
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7954e2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1810... Discriminator Loss: 1.5904... Generator Loss: 0.5657\n",
      "Epoch 1/2-Step 1820... Discriminator Loss: 2.0948... Generator Loss: 0.2882\n",
      "Epoch 1/2-Step 1830... Discriminator Loss: 1.0161... Generator Loss: 1.1578\n",
      "Epoch 1/2-Step 1840... Discriminator Loss: 1.3440... Generator Loss: 0.5831\n",
      "Epoch 1/2-Step 1850... Discriminator Loss: 1.0551... Generator Loss: 0.9924\n",
      "Epoch 1/2-Step 1860... Discriminator Loss: 1.3557... Generator Loss: 0.5949\n",
      "Epoch 1/2-Step 1870... Discriminator Loss: 2.1658... Generator Loss: 0.7761\n",
      "Epoch 2/2-Step 1880... Discriminator Loss: 1.7988... Generator Loss: 0.6089\n",
      "Epoch 2/2-Step 1890... Discriminator Loss: 1.3895... Generator Loss: 1.2412\n",
      "Epoch 2/2-Step 1900... Discriminator Loss: 1.1182... Generator Loss: 1.1952\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e7925cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 1910... Discriminator Loss: 1.4968... Generator Loss: 0.5684\n",
      "Epoch 2/2-Step 1920... Discriminator Loss: 1.4977... Generator Loss: 0.6296\n",
      "Epoch 2/2-Step 1930... Discriminator Loss: 1.9247... Generator Loss: 0.2941\n",
      "Epoch 2/2-Step 1940... Discriminator Loss: 0.8135... Generator Loss: 1.0944\n",
      "Epoch 2/2-Step 1950... Discriminator Loss: 2.9539... Generator Loss: 0.1693\n",
      "Epoch 2/2-Step 1960... Discriminator Loss: 1.6268... Generator Loss: 0.4130\n",
      "Epoch 2/2-Step 1970... Discriminator Loss: 2.4348... Generator Loss: 0.1656\n",
      "Epoch 2/2-Step 1980... Discriminator Loss: 1.0910... Generator Loss: 1.1037\n",
      "Epoch 2/2-Step 1990... Discriminator Loss: 2.4345... Generator Loss: 0.2697\n",
      "Epoch 2/2-Step 2000... Discriminator Loss: 2.8119... Generator Loss: 0.1547\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nd/x0Ka3+wGQSVtOxghq
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e79a53c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2010... Discriminator Loss: 2.9430... Generator Loss: 0.1119\n",
      "Epoch 2/2-Step 2020... Discriminator Loss: 2.4929... Generator Loss: 0.2289\n",
      "Epoch 2/2-Step 2030... Discriminator Loss: 1.3928... Generator Loss: 0.6620\n",
      "Epoch 2/2-Step 2040... Discriminator Loss: 0.9953... Generator Loss: 0.9853\n",
      "Epoch 2/2-Step 2050... Discriminator Loss: 2.0209... Generator Loss: 0.3261\n",
      "Epoch 2/2-Step 2060... Discriminator Loss: 1.0119... Generator Loss: 1.4060\n",
      "Epoch 2/2-Step 2070... Discriminator Loss: 2.3089... Generator Loss: 0.2537\n",
      "Epoch 2/2-Step 2080... Discriminator Loss: 1.8246... Generator Loss: 3.4251\n",
      "Epoch 2/2-Step 2090... Discriminator Loss: 3.6070... Generator Loss: 0.2074\n",
      "Epoch 2/2-Step 2100... Discriminator Loss: 1.7052... Generator Loss: 1.4942\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWpSyGXicc4OAvwGTAfH
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8110c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2110... Discriminator Loss: 1.7078... Generator Loss: 0.4842\n",
      "Epoch 2/2-Step 2120... Discriminator Loss: 1.8781... Generator Loss: 0.3792\n",
      "Epoch 2/2-Step 2130... Discriminator Loss: 0.9712... Generator Loss: 1.0053\n",
      "Epoch 2/2-Step 2140... Discriminator Loss: 2.0136... Generator Loss: 0.3907\n",
      "Epoch 2/2-Step 2150... Discriminator Loss: 2.0800... Generator Loss: 0.4026\n",
      "Epoch 2/2-Step 2160... Discriminator Loss: 1.2040... Generator Loss: 0.6960\n",
      "Epoch 2/2-Step 2170... Discriminator Loss: 0.4719... Generator Loss: 3.4201\n",
      "Epoch 2/2-Step 2180... Discriminator Loss: 1.2094... Generator Loss: 0.7431\n",
      "Epoch 2/2-Step 2190... Discriminator Loss: 1.9730... Generator Loss: 0.5210\n",
      "Epoch 2/2-Step 2200... Discriminator Loss: 1.3208... Generator Loss: 0.9190\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\np0op19QvK4An20pre+B
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e830072b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2210... Discriminator Loss: 0.9230... Generator Loss: 1.0733\n",
      "Epoch 2/2-Step 2220... Discriminator Loss: 1.6415... Generator Loss: 0.4990\n",
      "Epoch 2/2-Step 2230... Discriminator Loss: 1.2177... Generator Loss: 0.7985\n",
      "Epoch 2/2-Step 2240... Discriminator Loss: 1.7188... Generator Loss: 0.4603\n",
      "Epoch 2/2-Step 2250... Discriminator Loss: 1.0832... Generator Loss: 0.9234\n",
      "Epoch 2/2-Step 2260... Discriminator Loss: 2.4330... Generator Loss: 0.2552\n",
      "Epoch 2/2-Step 2270... Discriminator Loss: 1.0972... Generator Loss: 1.8646\n",
      "Epoch 2/2-Step 2280... Discriminator Loss: 4.2175... Generator Loss: 0.0387\n",
      "Epoch 2/2-Step 2290... Discriminator Loss: 1.4911... Generator Loss: 0.6146\n",
      "Epoch 2/2-Step 2300... Discriminator Loss: 1.0731... Generator Loss: 1.9281\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9yAJlPSMn22lNRCYRJH
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8306f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2310... Discriminator Loss: 2.6048... Generator Loss: 0.1825\n",
      "Epoch 2/2-Step 2320... Discriminator Loss: 1.3122... Generator Loss: 0.5521\n",
      "Epoch 2/2-Step 2330... Discriminator Loss: 1.3775... Generator Loss: 0.6418\n",
      "Epoch 2/2-Step 2340... Discriminator Loss: 1.1761... Generator Loss: 0.7925\n",
      "Epoch 2/2-Step 2350... Discriminator Loss: 1.1322... Generator Loss: 0.7320\n",
      "Epoch 2/2-Step 2360... Discriminator Loss: 0.1161... Generator Loss: 3.6323\n",
      "Epoch 2/2-Step 2370... Discriminator Loss: 1.0060... Generator Loss: 1.2201\n",
      "Epoch 2/2-Step 2380... Discriminator Loss: 0.6671... Generator Loss: 1.1994\n",
      "Epoch 2/2-Step 2390... Discriminator Loss: 3.2159... Generator Loss: 0.2751\n",
      "Epoch 2/2-Step 2400... Discriminator Loss: 3.4933... Generator Loss: 0.0844\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXHQ9BuS0hZ51R40aBcD
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e83075f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2410... Discriminator Loss: 0.9275... Generator Loss: 2.6940\n",
      "Epoch 2/2-Step 2420... Discriminator Loss: 1.1644... Generator Loss: 0.7646\n",
      "Epoch 2/2-Step 2430... Discriminator Loss: 1.8302... Generator Loss: 0.3799\n",
      "Epoch 2/2-Step 2440... Discriminator Loss: 1.7304... Generator Loss: 0.3922\n",
      "Epoch 2/2-Step 2450... Discriminator Loss: 2.1595... Generator Loss: 0.3598\n",
      "Epoch 2/2-Step 2460... Discriminator Loss: 1.7138... Generator Loss: 1.4030\n",
      "Epoch 2/2-Step 2470... Discriminator Loss: 1.2481... Generator Loss: 0.6815\n",
      "Epoch 2/2-Step 2480... Discriminator Loss: 1.4053... Generator Loss: 0.7218\n",
      "Epoch 2/2-Step 2490... Discriminator Loss: 3.3293... Generator Loss: 0.1335\n",
      "Epoch 2/2-Step 2500... Discriminator Loss: 1.8164... Generator Loss: 0.5824\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQafQ4qQFveOnS2ntCEw
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e8307ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2510... Discriminator Loss: 2.6035... Generator Loss: 0.2349\n",
      "Epoch 2/2-Step 2520... Discriminator Loss: 0.8650... Generator Loss: 4.0960\n",
      "Epoch 2/2-Step 2530... Discriminator Loss: 1.5684... Generator Loss: 0.5153\n",
      "Epoch 2/2-Step 2540... Discriminator Loss: 0.7783... Generator Loss: 1.2478\n",
      "Epoch 2/2-Step 2550... Discriminator Loss: 2.4847... Generator Loss: 0.3230\n",
      "Epoch 2/2-Step 2560... Discriminator Loss: 0.8980... Generator Loss: 4.6620\n",
      "Epoch 2/2-Step 2570... Discriminator Loss: 1.3185... Generator Loss: 1.2826\n",
      "Epoch 2/2-Step 2580... Discriminator Loss: 3.2025... Generator Loss: 0.2373\n",
      "Epoch 2/2-Step 2590... Discriminator Loss: 2.0644... Generator Loss: 0.4936\n",
      "Epoch 2/2-Step 2600... Discriminator Loss: 0.9851... Generator Loss: 1.2821\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLj8uI7QOu7311luB2sq
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78bfcc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2610... Discriminator Loss: 1.7005... Generator Loss: 0.7132\n",
      "Epoch 2/2-Step 2620... Discriminator Loss: 1.7881... Generator Loss: 0.4661\n",
      "Epoch 2/2-Step 2630... Discriminator Loss: 1.1388... Generator Loss: 0.9582\n",
      "Epoch 2/2-Step 2640... Discriminator Loss: 1.0748... Generator Loss: 2.2080\n",
      "Epoch 2/2-Step 2650... Discriminator Loss: 1.4443... Generator Loss: 0.7525\n",
      "Epoch 2/2-Step 2660... Discriminator Loss: 2.8320... Generator Loss: 0.1446\n",
      "Epoch 2/2-Step 2670... Discriminator Loss: 1.6948... Generator Loss: 0.9054\n",
      "Epoch 2/2-Step 2680... Discriminator Loss: 0.5985... Generator Loss: 1.3193\n",
      "Epoch 2/2-Step 2690... Discriminator Loss: 1.9464... Generator Loss: 2.4039\n",
      "Epoch 2/2-Step 2700... Discriminator Loss: 1.1410... Generator Loss: 0.8293\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78f5bba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2710... Discriminator Loss: 0.9968... Generator Loss: 0.9076\n",
      "Epoch 2/2-Step 2720... Discriminator Loss: 1.7824... Generator Loss: 0.5328\n",
      "Epoch 2/2-Step 2730... Discriminator Loss: 0.2981... Generator Loss: 2.5057\n",
      "Epoch 2/2-Step 2740... Discriminator Loss: 1.0728... Generator Loss: 0.9905\n",
      "Epoch 2/2-Step 2750... Discriminator Loss: 1.5693... Generator Loss: 0.5073\n",
      "Epoch 2/2-Step 2760... Discriminator Loss: 1.0690... Generator Loss: 3.7526\n",
      "Epoch 2/2-Step 2770... Discriminator Loss: 2.4759... Generator Loss: 0.2352\n",
      "Epoch 2/2-Step 2780... Discriminator Loss: 0.9367... Generator Loss: 2.4314\n",
      "Epoch 2/2-Step 2790... Discriminator Loss: 1.5539... Generator Loss: 2.2785\n",
      "Epoch 2/2-Step 2800... Discriminator Loss: 1.4832... Generator Loss: 3.2962\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJfaXDzGQQS5ARdQ+SBY
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78ccbc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2810... Discriminator Loss: 1.1598... Generator Loss: 0.6267\n",
      "Epoch 2/2-Step 2820... Discriminator Loss: 1.3759... Generator Loss: 0.5195\n",
      "Epoch 2/2-Step 2830... Discriminator Loss: 1.0197... Generator Loss: 0.9089\n",
      "Epoch 2/2-Step 2840... Discriminator Loss: 0.5584... Generator Loss: 1.6436\n",
      "Epoch 2/2-Step 2850... Discriminator Loss: 1.8377... Generator Loss: 0.3658\n",
      "Epoch 2/2-Step 2860... Discriminator Loss: 1.2147... Generator Loss: 0.6694\n",
      "Epoch 2/2-Step 2870... Discriminator Loss: 1.1408... Generator Loss: 0.8225\n",
      "Epoch 2/2-Step 2880... Discriminator Loss: 0.8431... Generator Loss: 1.3163\n",
      "Epoch 2/2-Step 2890... Discriminator Loss: 2.2990... Generator Loss: 0.2128\n",
      "Epoch 2/2-Step 2900... Discriminator Loss: 2.0544... Generator Loss: 0.4308\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3FsELMq2P3fOzQIGUGY
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e789c6080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 2910... Discriminator Loss: 2.3895... Generator Loss: 0.2125\n",
      "Epoch 2/2-Step 2920... Discriminator Loss: 0.8310... Generator Loss: 1.0606\n",
      "Epoch 2/2-Step 2930... Discriminator Loss: 2.2490... Generator Loss: 4.4028\n",
      "Epoch 2/2-Step 2940... Discriminator Loss: 1.2407... Generator Loss: 1.2902\n",
      "Epoch 2/2-Step 2950... Discriminator Loss: 2.6363... Generator Loss: 0.2990\n",
      "Epoch 2/2-Step 2960... Discriminator Loss: 2.1573... Generator Loss: 0.2985\n",
      "Epoch 2/2-Step 2970... Discriminator Loss: 1.2197... Generator Loss: 0.8472\n",
      "Epoch 2/2-Step 2980... Discriminator Loss: 1.1789... Generator Loss: 2.3603\n",
      "Epoch 2/2-Step 2990... Discriminator Loss: 1.4132... Generator Loss: 1.0563\n",
      "Epoch 2/2-Step 3000... Discriminator Loss: 1.4316... Generator Loss: 2.1424\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAGyyySZB9swzzwCl02z
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e789c6630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3010... Discriminator Loss: 2.1096... Generator Loss: 0.3688\n",
      "Epoch 2/2-Step 3020... Discriminator Loss: 2.7113... Generator Loss: 0.1992\n",
      "Epoch 2/2-Step 3030... Discriminator Loss: 0.7655... Generator Loss: 1.4882\n",
      "Epoch 2/2-Step 3040... Discriminator Loss: 0.8179... Generator Loss: 1.1351\n",
      "Epoch 2/2-Step 3050... Discriminator Loss: 2.7765... Generator Loss: 0.1775\n",
      "Epoch 2/2-Step 3060... Discriminator Loss: 1.6212... Generator Loss: 2.3479\n",
      "Epoch 2/2-Step 3070... Discriminator Loss: 1.7499... Generator Loss: 0.4827\n",
      "Epoch 2/2-Step 3080... Discriminator Loss: 1.0921... Generator Loss: 1.2112\n",
      "Epoch 2/2-Step 3090... Discriminator Loss: 2.1500... Generator Loss: 2.5354\n",
      "Epoch 2/2-Step 3100... Discriminator Loss: 1.4504... Generator Loss: 0.6992\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIhQD6dprrx1kol3pMuN
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78a1dda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3110... Discriminator Loss: 0.4549... Generator Loss: 2.0005\n",
      "Epoch 2/2-Step 3120... Discriminator Loss: 2.3352... Generator Loss: 0.1726\n",
      "Epoch 2/2-Step 3130... Discriminator Loss: 1.8152... Generator Loss: 0.3370\n",
      "Epoch 2/2-Step 3140... Discriminator Loss: 1.2448... Generator Loss: 0.6605\n",
      "Epoch 2/2-Step 3150... Discriminator Loss: 1.3393... Generator Loss: 0.6704\n",
      "Epoch 2/2-Step 3160... Discriminator Loss: 0.3832... Generator Loss: 1.5856\n",
      "Epoch 2/2-Step 3170... Discriminator Loss: 1.4028... Generator Loss: 0.6451\n",
      "Epoch 2/2-Step 3180... Discriminator Loss: 0.6644... Generator Loss: 1.8764\n",
      "Epoch 2/2-Step 3190... Discriminator Loss: 0.9284... Generator Loss: 0.8118\n",
      "Epoch 2/2-Step 3200... Discriminator Loss: 0.9369... Generator Loss: 1.0325\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntNLIr7ae2Q866KDQboa
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e785cea20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3210... Discriminator Loss: 3.6743... Generator Loss: 0.0663\n",
      "Epoch 2/2-Step 3220... Discriminator Loss: 1.6144... Generator Loss: 0.5525\n",
      "Epoch 2/2-Step 3230... Discriminator Loss: 0.6816... Generator Loss: 1.5077\n",
      "Epoch 2/2-Step 3240... Discriminator Loss: 3.3266... Generator Loss: 0.0811\n",
      "Epoch 2/2-Step 3250... Discriminator Loss: 1.7431... Generator Loss: 0.5593\n",
      "Epoch 2/2-Step 3260... Discriminator Loss: 1.4966... Generator Loss: 0.5404\n",
      "Epoch 2/2-Step 3270... Discriminator Loss: 1.6944... Generator Loss: 0.7823\n",
      "Epoch 2/2-Step 3280... Discriminator Loss: 1.5925... Generator Loss: 0.6031\n",
      "Epoch 2/2-Step 3290... Discriminator Loss: 1.2572... Generator Loss: 0.9174\n",
      "Epoch 2/2-Step 3300... Discriminator Loss: 1.7342... Generator Loss: 0.5187\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfuaZZwAYOnRokGn1VLI
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e788973c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3310... Discriminator Loss: 0.1825... Generator Loss: 4.5827\n",
      "Epoch 2/2-Step 3320... Discriminator Loss: 1.2775... Generator Loss: 0.7300\n",
      "Epoch 2/2-Step 3330... Discriminator Loss: 0.8732... Generator Loss: 1.2389\n",
      "Epoch 2/2-Step 3340... Discriminator Loss: 1.8792... Generator Loss: 0.4560\n",
      "Epoch 2/2-Step 3350... Discriminator Loss: 0.8879... Generator Loss: 0.9273\n",
      "Epoch 2/2-Step 3360... Discriminator Loss: 0.4954... Generator Loss: 1.6400\n",
      "Epoch 2/2-Step 3370... Discriminator Loss: 0.6375... Generator Loss: 1.5675\n",
      "Epoch 2/2-Step 3380... Discriminator Loss: 1.5641... Generator Loss: 0.5521\n",
      "Epoch 2/2-Step 3390... Discriminator Loss: 0.4753... Generator Loss: 1.8465\n",
      "Epoch 2/2-Step 3400... Discriminator Loss: 0.4872... Generator Loss: 1.5438\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOQ1xG9fIK442rmZFj3+
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e781a3048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3410... Discriminator Loss: 2.7610... Generator Loss: 0.2020\n",
      "Epoch 2/2-Step 3420... Discriminator Loss: 1.1922... Generator Loss: 1.7685\n",
      "Epoch 2/2-Step 3430... Discriminator Loss: 0.8931... Generator Loss: 1.5826\n",
      "Epoch 2/2-Step 3440... Discriminator Loss: 2.0843... Generator Loss: 0.4748\n",
      "Epoch 2/2-Step 3450... Discriminator Loss: 1.3589... Generator Loss: 0.6113\n",
      "Epoch 2/2-Step 3460... Discriminator Loss: 0.7527... Generator Loss: 1.3879\n",
      "Epoch 2/2-Step 3470... Discriminator Loss: 1.2510... Generator Loss: 0.7047\n",
      "Epoch 2/2-Step 3480... Discriminator Loss: 1.1979... Generator Loss: 0.8418\n",
      "Epoch 2/2-Step 3490... Discriminator Loss: 1.6452... Generator Loss: 5.2290\n",
      "Epoch 2/2-Step 3500... Discriminator Loss: 0.5588... Generator Loss: 1.8905\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6noCsde34cOHB5lc37F
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e78212fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3510... Discriminator Loss: 1.0099... Generator Loss: 1.4121\n",
      "Epoch 2/2-Step 3520... Discriminator Loss: 2.7485... Generator Loss: 0.2938\n",
      "Epoch 2/2-Step 3530... Discriminator Loss: 1.3084... Generator Loss: 0.5821\n",
      "Epoch 2/2-Step 3540... Discriminator Loss: 1.4313... Generator Loss: 3.7524\n",
      "Epoch 2/2-Step 3550... Discriminator Loss: 0.9619... Generator Loss: 2.8347\n",
      "Epoch 2/2-Step 3560... Discriminator Loss: 0.8457... Generator Loss: 1.6197\n",
      "Epoch 2/2-Step 3570... Discriminator Loss: 1.3874... Generator Loss: 0.6073\n",
      "Epoch 2/2-Step 3580... Discriminator Loss: 1.4953... Generator Loss: 0.6685\n",
      "Epoch 2/2-Step 3590... Discriminator Loss: 1.2233... Generator Loss: 0.6353\n",
      "Epoch 2/2-Step 3600... Discriminator Loss: 1.0592... Generator Loss: 1.0551\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxmv1PpbsUsfFl2FVZzN
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e77f44c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3610... Discriminator Loss: 1.9337... Generator Loss: 0.3964\n",
      "Epoch 2/2-Step 3620... Discriminator Loss: 0.7413... Generator Loss: 1.2235\n",
      "Epoch 2/2-Step 3630... Discriminator Loss: 1.1959... Generator Loss: 0.9519\n",
      "Epoch 2/2-Step 3640... Discriminator Loss: 1.0010... Generator Loss: 1.5190\n",
      "Epoch 2/2-Step 3650... Discriminator Loss: 2.8820... Generator Loss: 0.1989\n",
      "Epoch 2/2-Step 3660... Discriminator Loss: 3.1258... Generator Loss: 0.2029\n",
      "Epoch 2/2-Step 3670... Discriminator Loss: 1.7464... Generator Loss: 0.5479\n",
      "Epoch 2/2-Step 3680... Discriminator Loss: 1.9657... Generator Loss: 0.3596\n",
      "Epoch 2/2-Step 3690... Discriminator Loss: 2.0325... Generator Loss: 0.4481\n",
      "Epoch 2/2-Step 3700... Discriminator Loss: 0.7904... Generator Loss: 1.4904\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nW0uM5557Lku3G0YtPeZ
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7e781ae3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3710... Discriminator Loss: 3.2823... Generator Loss: 0.0943\n",
      "Epoch 2/2-Step 3720... Discriminator Loss: 3.5929... Generator Loss: 0.1268\n",
      "Epoch 2/2-Step 3730... Discriminator Loss: 2.5083... Generator Loss: 0.2654\n",
      "Epoch 2/2-Step 3740... Discriminator Loss: 1.2404... Generator Loss: 0.8706\n",
      "Epoch 2/2-Step 3750... Discriminator Loss: 0.4249... Generator Loss: 3.3969\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "z_dim = 100\n",
    "learning_rate = 0.001\n",
    "beta1 = 0.5\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "epochs = 2\n",
    "\n",
    "mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))\n",
    "with tf.Graph().as_default():\n",
    "    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,\n",
    "          mnist_dataset.shape, mnist_dataset.image_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CelebA\n",
    "Run your GANs on CelebA.  It will take around 20 minutes on the average GPU to run one epoch.  You can run the whole epoch or stop when it starts to generate realistic faces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10... Discriminator Loss: 0.0727... Generator Loss: 9.3794\n",
      "Epoch 1/1-Step 20... Discriminator Loss: 0.0076... Generator Loss: 12.2277\n",
      "Epoch 1/1-Step 30... Discriminator Loss: 0.0632... Generator Loss: 3.0810\n",
      "Epoch 1/1-Step 40... Discriminator Loss: 0.0050... Generator Loss: 6.2162\n",
      "Epoch 1/1-Step 50... Discriminator Loss: 0.0349... Generator Loss: 5.5600\n",
      "Epoch 1/1-Step 60... Discriminator Loss: 0.0111... Generator Loss: 4.7894\n",
      "Epoch 1/1-Step 70... Discriminator Loss: 0.0235... Generator Loss: 5.5622\n",
      "Epoch 1/1-Step 80... Discriminator Loss: 0.1791... Generator Loss: 1.9836\n",
      "Epoch 1/1-Step 90... Discriminator Loss: 0.0956... Generator Loss: 6.7089\n",
      "Epoch 1/1-Step 100... Discriminator Loss: 0.6838... Generator Loss: 8.5993\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsfXe8XVWV/9rn3PZ6zUsPCSEEAgRCB0EQBBVULIiVUUFw\ndHTsjmUYK47OiGJX7DqCooKCIEUg9JZAAum95/Vy+72n7N8fa5/9Xdd3Q4LgU37vrM+HTzbr7rPb\n2e/s715Vaa0ppphimlzk/KMHEFNMMU08xX/4McU0CSn+w48ppklI8R9+TDFNQor/8GOKaRJS/Icf\nU0yTkOI//JhimoT0nP7wlVIvV0qtV0ptUkp94vkaVEwxxfT3JfW3GvAopVwi2kBE5xDRLiJ6nIje\nrLVe8/wNL6aYYvp7UOI5PHsiEW3SWm8hIlJK/ZqILiCiff7hJ5KOTqe5y2oYEBFREODDo+p9g5Sy\nRU26Tj1V84+sR0SkwvHtRGVtfxT1ZN2avsN91xNU07ctyr7H81zXteWkww9VQ3QUBPvp26kzn6BO\n346Yj6mLeqLNZ7OW9eo9l/dYUxy/RtH6EO1jjfa3N0yF5+897ms+49s+0DXad73x84n+x3V4fQLP\np8APxk/or+i5/OHPJKKd4v93EdFJz/RAOp2gwxZ3ExHRnmKBiIhGR6v2d8cz45UXkATmEJq35VbE\n746pLGbiO9jNiQI/rxL449Lm4+MlyxhbTrygBP+u0mjU01w3WRHjif6QXPACjb7tOF1MSCV5HEpM\nsr2p05anN/NDO8z6EBGN5CpmLpijEh8LajQf07SYz7AZUwJz0BmUq26J6+XEYptx6hTa9gnvJ1Ey\nG1PMN5p7zbw9/Gw3s3yPVOc9ivdDCR6HI9eohdeopwkP7RJrNJZlviPej4reT1K8H7M33JKYQ6LO\nfJR8j9G+HF9P1nU9sYeiv1FZj/C1sXtd/omacYSynlgjFe2jBNbFIV639uYOIiLq3bSHDoSeyx/+\nAZFS6nIiupyIKCU2VEwxxfSPo+fyh7+biGaL/59leDWktb6GiK4hIkpmXL17IE9ERNkB/pRVSz4q\nRx+yEJ9BR8uTwuCehMA/BtW4Wp66+GJ6qYiJr2Q69E17+KoX0gJeBcxPhjjtfNNmNSEwomlToE8K\npcwkGqeYj2s+8Y5KWt5IZsSWqx3cfmEAfXvlau1c/mo+mYHA9C3mkzHQ1wcvE2CttalbyICnTJtJ\ngRR9cfpU3XDcfJwwOqXEvF25IGa+evyJX7P75Bpps64O1mh0lNfI68J8cv1Yo2rJ22ebbp09VJVj\n1FhLNxx/6lbrvMea+UTvvO68BUuuUZ29HsF6CfVr5hPtdQETon0U7aHAk3e3fdNzkeo/TkQLlFLz\nlFIpInoTEd30HNqLKaaYJoj+Zqk+EZFS6jwiupqIXCL6idb6ymeq7yYd3dTOx1Yxy1/owBcCqegr\nKj5HNcOzghkpsDL1pLBGfGbd6PTC4UFhxHPxUMpHp4H5ygZSRmLuho7gRQeFHKMW0hg7Tvl5NXNU\n4nRodjN4pp37yQ/hJA6MEMsVfQvAgHHK+Zg7ZCjriTlql/upmXfyr9ojqpH+2dNdvh8r8JMCqfFC\nsLrvUdRTNe/cnJZijVrcBu67VaCaUbFGBtnU9B21WTOeSFA6/j3W1K0R/Kpx9fa7L6N5k6Q6aySF\nhNr+OL6enI9ECQaNNDm8hwrVMgWhHEh9ek53fK31rUR063NpI6aYYpp4ii33YoppEtLfXapfQ5rI\nytMiNCOgjEU/eh9QRydMPfzuWIGWqBc22WKQYDiYCqEXiZpX4QLL85L9ttyos0REVBLQTOsWbtop\njetbwj4lllQ7oakH2BjVdShteVUHUrvGsmlfXl10g5kLOsqEUN2Fdj7Ho83UdiIi6gwHLG9MNhou\nMfW2WVZTOEpERMUaHNuIR4xuSenxAiSthbq05v2E5nfUVebeFa0P10ObWCO8x4oR9CWrRRpX0dTm\nNgWnTt9k+0Z/cr/h6ov5hI4eV69Wv276lhCdxl9ntDhnrT2BbNPsYa0T4+rxfMx+q+mZ94Zv9pAW\n6tdnovjEjymmSUjPSbj3bMlNOLqpjU+6YtEI9zwp3LMlPCQwSTRWVSN8MsKyDL5hoSuML4rmy60g\nQEsv5lOsrIfRzXpIwcKAn3Fnos3qcI6byYsvuBm6qhEcSuu5CMIIA56UGW8S9Zo0TtWkUcPlAqGq\nqjJqccvSDqLBllLntvN8FIw3kveaEyBos7zGy6fZcm49G1gmHxTz9nicagb68YayGHtkLyNPMfN+\ndCjnLYYZvcukPO2MoNSTMij0qZpMOQHhXWvIaxQI66BiCWW/aoR7YZ29kZLvzAzOl32P32818wnr\nSOrEq7D7UqK0qE1nfD2ifex102YNcpDjNPvIyYjOkzyfFrOHcvk8+QdguRef+DHFNAkp/sOPKaZJ\nSBMr3CMibeBXZCElfHTIyocEZEoI1BJEWAuo3UK2pirgT1FgsnAe/+vm8ciLtjJMftgDzC2ejE4T\nq3lZunej7z4zjiAjBlzm72ZC2KZ7cj7p6GoCZtKMV4fCHr5JCJcMnOsoQPg3YKyxgkPRtpvF2N71\nQCsREf3CByzPfYKfaX8S7Xz5t1i4zxoBaN8rAKfTy3hM04W593a5/tHchf140sD12nmjHEHVJg9n\nTPR+dLN4SMikWozc0nOFb0GzaU9cMxICBkc3LJ2SFpjj63kRbE+KvqXxqKkbyvkkovco5iWvFJHF\nnTxGTQNOnXrcZlQPvKSxnPSFRanGjc723yDm45uGPCMH1fsF+UzxiR9TTJOQ4j/8mGKahDSxUF8R\nOUZ3G0R6zHrWoUJoKYTkpMxo3bJ4yMDPgsCabgug0pxtjDv1QjT0cC8/nzoYuvDTH2i25dVzGLv3\njgKzqjQ/31jAt7JiYKVXY0KJoSWicQro6xlYmRAS664qIHjQzWMazApc2MR9z1mPxqccC7z9yy2s\nccicPGZ53/wS+0+t+RDg/9dWzrDl7lNZZ3/ZDZD0//YQfn6Lgl2B04h1yxgX3opwaLI+IQI6Zyri\nPDFzFwJ4Uhl+qCOH+VQE7C+U+PlUBjYTc0u8RnuaMZ6yVKabPeNWpVmzqh0jwVTaEfXErQsQX9oD\nRNB6nya75h+pzXCi9sTVQ5iSR20qYZvhmyuhEmvZUBTaEAPnS8L1OJXmfXBQme8EW/ZvrfvXU4kp\nppgmC/0D9Ph8mpTyfOL5/nhXRVXj9SJbMMI4oQN3g8h6TnzD1EF4fCqfGrMLfZY3UOZ2HOe9llee\neYstL+jbREREW0SwhkDzaRkkYQmX9PhrK/yMSCshjTFSP1e4w4ZG+pJMtFiek8EJ257gU3dwFMeH\nT6/jwiycgJeUMd47hng+PVNxuq9+1wNERPT0fa+wvIsfQz/JpmVERPT4ER+xvAtX3U5ERL+HlzD5\nNBfl5F4iIkp7GEc1GqYz0/J0CgEy2iJrQKmzd87n9hqWWdZUr9eWh4wgMJM6xPLCBl7LhIbnd74g\nnXQiRyXRTTjeslKTWQNXuCOH4y0r5ZmoI8u9cF9/K3Us9yIjjxoHIWHdaNp061h1EnWAlwayawjY\narEqXcxTBsUZ0FjK9lLgV/d77McnfkwxTUKK//BjimkS0sRD/XZjslv4G0x2I8cH6Zdu4JNqFya7\nMuZe3sTPS7ZaXuoVU3kM+c3gPSKgXZXHqA4SwqehISIicgbFeKLrhUD3oQ8ICQGSMNltjMxRMcaW\nEA2km7lufqYQdrUzdM48OgXtdL7Dls9b8yYiIrrX+7DlHf+56URElBs7xvKm/w8CJt267INERNT4\nQcD/kvFT0kdiPqWNgNaOveXA/sGZymvkE+B/YlDEF3DZnLj53V2WN7Z5I7eyFOsbBngmdRxD3crw\nDsvr7GfJVtnDNaJQz2S3njl3SuwNA/+VX0c6R4T4DlIoF+67HpHYl/VMdt3x9biuGZs0VW7lRkMl\n9lBeXmF5XyamCmFwggW
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38733be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 110... Discriminator Loss: 0.5457... Generator Loss: 2.6397\n",
      "Epoch 1/1-Step 120... Discriminator Loss: 0.8652... Generator Loss: 0.9929\n",
      "Epoch 1/1-Step 130... Discriminator Loss: 2.4754... Generator Loss: 8.4404\n",
      "Epoch 1/1-Step 140... Discriminator Loss: 4.9178... Generator Loss: 0.0960\n",
      "Epoch 1/1-Step 150... Discriminator Loss: 4.8412... Generator Loss: 0.0560\n",
      "Epoch 1/1-Step 160... Discriminator Loss: 3.6360... Generator Loss: 0.1029\n",
      "Epoch 1/1-Step 170... Discriminator Loss: 2.6070... Generator Loss: 0.1986\n",
      "Epoch 1/1-Step 180... Discriminator Loss: 2.5153... Generator Loss: 0.2089\n",
      "Epoch 1/1-Step 190... Discriminator Loss: 2.1722... Generator Loss: 0.5444\n",
      "Epoch 1/1-Step 200... Discriminator Loss: 2.5551... Generator Loss: 0.2539\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd388fce0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 210... Discriminator Loss: 2.0813... Generator Loss: 0.3078\n",
      "Epoch 1/1-Step 220... Discriminator Loss: 1.9055... Generator Loss: 0.4443\n",
      "Epoch 1/1-Step 230... Discriminator Loss: 2.5085... Generator Loss: 0.3532\n",
      "Epoch 1/1-Step 240... Discriminator Loss: 2.2245... Generator Loss: 0.2461\n",
      "Epoch 1/1-Step 250... Discriminator Loss: 2.1935... Generator Loss: 0.4254\n",
      "Epoch 1/1-Step 260... Discriminator Loss: 1.9623... Generator Loss: 0.3481\n",
      "Epoch 1/1-Step 270... Discriminator Loss: 1.9798... Generator Loss: 0.3965\n",
      "Epoch 1/1-Step 280... Discriminator Loss: 1.8648... Generator Loss: 0.3960\n",
      "Epoch 1/1-Step 290... Discriminator Loss: 2.0754... Generator Loss: 0.4496\n",
      "Epoch 1/1-Step 300... Discriminator Loss: 1.7719... Generator Loss: 0.5716\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIgSFdb9elJp/EoqOYd
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd389f2ab70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 310... Discriminator Loss: 1.6204... Generator Loss: 0.5335\n",
      "Epoch 1/1-Step 320... Discriminator Loss: 2.0243... Generator Loss: 0.3130\n",
      "Epoch 1/1-Step 330... Discriminator Loss: 1.6476... Generator Loss: 0.8068\n",
      "Epoch 1/1-Step 340... Discriminator Loss: 1.7978... Generator Loss: 0.4309\n",
      "Epoch 1/1-Step 350... Discriminator Loss: 2.0922... Generator Loss: 0.3991\n",
      "Epoch 1/1-Step 360... Discriminator Loss: 1.6118... Generator Loss: 0.6267\n",
      "Epoch 1/1-Step 370... Discriminator Loss: 1.6625... Generator Loss: 0.6424\n",
      "Epoch 1/1-Step 380... Discriminator Loss: 1.7148... Generator Loss: 0.4611\n",
      "Epoch 1/1-Step 390... Discriminator Loss: 1.5929... Generator Loss: 0.5055\n",
      "Epoch 1/1-Step 400... Discriminator Loss: 1.6716... Generator Loss: 0.5413\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3858ae828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 410... Discriminator Loss: 1.5165... Generator Loss: 0.5024\n",
      "Epoch 1/1-Step 420... Discriminator Loss: 1.6313... Generator Loss: 0.5201\n",
      "Epoch 1/1-Step 430... Discriminator Loss: 1.6951... Generator Loss: 0.3886\n",
      "Epoch 1/1-Step 440... Discriminator Loss: 2.0725... Generator Loss: 0.3885\n",
      "Epoch 1/1-Step 450... Discriminator Loss: 1.7706... Generator Loss: 0.4545\n",
      "Epoch 1/1-Step 460... Discriminator Loss: 1.6135... Generator Loss: 0.4660\n",
      "Epoch 1/1-Step 470... Discriminator Loss: 1.7225... Generator Loss: 0.5455\n",
      "Epoch 1/1-Step 480... Discriminator Loss: 1.8447... Generator Loss: 0.5592\n",
      "Epoch 1/1-Step 490... Discriminator Loss: 1.5571... Generator Loss: 0.6380\n",
      "Epoch 1/1-Step 500... Discriminator Loss: 1.6766... Generator Loss: 0.4717\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niIhKh/82TPSt70uZ8Sz
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd39ba142e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 510... Discriminator Loss: 1.6623... Generator Loss: 0.5098\n",
      "Epoch 1/1-Step 520... Discriminator Loss: 1.7112... Generator Loss: 0.5697\n",
      "Epoch 1/1-Step 530... Discriminator Loss: 1.7006... Generator Loss: 0.3925\n",
      "Epoch 1/1-Step 540... Discriminator Loss: 1.5537... Generator Loss: 0.4265\n",
      "Epoch 1/1-Step 550... Discriminator Loss: 1.7764... Generator Loss: 0.3655\n",
      "Epoch 1/1-Step 560... Discriminator Loss: 1.3598... Generator Loss: 0.5995\n",
      "Epoch 1/1-Step 570... Discriminator Loss: 1.4859... Generator Loss: 0.6381\n",
      "Epoch 1/1-Step 580... Discriminator Loss: 1.6684... Generator Loss: 0.4805\n",
      "Epoch 1/1-Step 590... Discriminator Loss: 1.7357... Generator Loss: 0.3859\n",
      "Epoch 1/1-Step 600... Discriminator Loss: 1.7320... Generator Loss: 0.5614\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd388de5e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 610... Discriminator Loss: 1.7415... Generator Loss: 0.3289\n",
      "Epoch 1/1-Step 620... Discriminator Loss: 1.7709... Generator Loss: 0.5224\n",
      "Epoch 1/1-Step 630... Discriminator Loss: 1.6991... Generator Loss: 0.4420\n",
      "Epoch 1/1-Step 640... Discriminator Loss: 1.6305... Generator Loss: 0.4226\n",
      "Epoch 1/1-Step 650... Discriminator Loss: 1.3594... Generator Loss: 0.6459\n",
      "Epoch 1/1-Step 660... Discriminator Loss: 1.5848... Generator Loss: 0.6701\n",
      "Epoch 1/1-Step 670... Discriminator Loss: 1.5340... Generator Loss: 0.5317\n",
      "Epoch 1/1-Step 680... Discriminator Loss: 1.4880... Generator Loss: 0.7570\n",
      "Epoch 1/1-Step 690... Discriminator Loss: 1.7384... Generator Loss: 0.4051\n",
      "Epoch 1/1-Step 700... Discriminator Loss: 1.6310... Generator Loss: 0.3631\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nk2Kstj4bV4ZM1DW6eo2
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3844060b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 710... Discriminator Loss: 1.5873... Generator Loss: 0.5126\n",
      "Epoch 1/1-Step 720... Discriminator Loss: 1.5325... Generator Loss: 0.5958\n",
      "Epoch 1/1-Step 730... Discriminator Loss: 1.3896... Generator Loss: 0.6622\n",
      "Epoch 1/1-Step 740... Discriminator Loss: 1.5005... Generator Loss: 0.7440\n",
      "Epoch 1/1-Step 750... Discriminator Loss: 1.7124... Generator Loss: 0.4646\n",
      "Epoch 1/1-Step 760... Discriminator Loss: 1.6152... Generator Loss: 0.6327\n",
      "Epoch 1/1-Step 770... Discriminator Loss: 1.6164... Generator Loss: 0.5734\n",
      "Epoch 1/1-Step 780... Discriminator Loss: 1.7138... Generator Loss: 0.4487\n",
      "Epoch 1/1-Step 790... Discriminator Loss: 1.5637... Generator Loss: 0.5049\n",
      "Epoch 1/1-Step 800... Discriminator Loss: 1.6402... Generator Loss: 0.5617\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3842c6dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 810... Discriminator Loss: 1.7563... Generator Loss: 0.3923\n",
      "Epoch 1/1-Step 820... Discriminator Loss: 1.7762... Generator Loss: 0.4678\n",
      "Epoch 1/1-Step 830... Discriminator Loss: 1.5962... Generator Loss: 0.5257\n",
      "Epoch 1/1-Step 840... Discriminator Loss: 1.6072... Generator Loss: 0.6522\n",
      "Epoch 1/1-Step 850... Discriminator Loss: 1.6226... Generator Loss: 0.5555\n",
      "Epoch 1/1-Step 860... Discriminator Loss: 1.5246... Generator Loss: 0.6241\n",
      "Epoch 1/1-Step 870... Discriminator Loss: 1.6424... Generator Loss: 0.5019\n",
      "Epoch 1/1-Step 880... Discriminator Loss: 1.5898... Generator Loss: 0.4438\n",
      "Epoch 1/1-Step 890... Discriminator Loss: 1.6457... Generator Loss: 0.5456\n",
      "Epoch 1/1-Step 900... Discriminator Loss: 1.6340... Generator Loss: 0.5472\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmIkdVCiRpA4nQ3+0pAa
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd385721f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 910... Discriminator Loss: 1.5515... Generator Loss: 0.5978\n",
      "Epoch 1/1-Step 920... Discriminator Loss: 1.6087... Generator Loss: 0.5024\n",
      "Epoch 1/1-Step 930... Discriminator Loss: 1.6616... Generator Loss: 0.4458\n",
      "Epoch 1/1-Step 940... Discriminator Loss: 1.9298... Generator Loss: 0.3957\n",
      "Epoch 1/1-Step 950... Discriminator Loss: 1.5688... Generator Loss: 0.6162\n",
      "Epoch 1/1-Step 960... Discriminator Loss: 1.5625... Generator Loss: 0.5942\n",
      "Epoch 1/1-Step 970... Discriminator Loss: 1.6850... Generator Loss: 0.3905\n",
      "Epoch 1/1-Step 980... Discriminator Loss: 1.5903... Generator Loss: 0.5228\n",
      "Epoch 1/1-Step 990... Discriminator Loss: 1.5929... Generator Loss: 0.5685\n",
      "Epoch 1/1-Step 1000... Discriminator Loss: 1.7475... Generator Loss: 0.3601\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3841e09e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1010... Discriminator Loss: 1.6496... Generator Loss: 0.4886\n",
      "Epoch 1/1-Step 1020... Discriminator Loss: 1.6259... Generator Loss: 0.4527\n",
      "Epoch 1/1-Step 1030... Discriminator Loss: 1.6950... Generator Loss: 0.4469\n",
      "Epoch 1/1-Step 1040... Discriminator Loss: 1.5717... Generator Loss: 0.6022\n",
      "Epoch 1/1-Step 1050... Discriminator Loss: 1.7170... Generator Loss: 0.5175\n",
      "Epoch 1/1-Step 1060... Discriminator Loss: 1.9889... Generator Loss: 0.4170\n",
      "Epoch 1/1-Step 1070... Discriminator Loss: 1.7334... Generator Loss: 0.4863\n",
      "Epoch 1/1-Step 1080... Discriminator Loss: 1.4844... Generator Loss: 0.6532\n",
      "Epoch 1/1-Step 1090... Discriminator Loss: 1.5442... Generator Loss: 0.5522\n",
      "Epoch 1/1-Step 1100... Discriminator Loss: 1.6279... Generator Loss: 0.5473\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nZRsirHC9oR6yvcL7R4m
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3842cd080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1110... Discriminator Loss: 1.4117... Generator Loss: 0.6320\n",
      "Epoch 1/1-Step 1120... Discriminator Loss: 1.5478... Generator Loss: 0.5325\n",
      "Epoch 1/1-Step 1130... Discriminator Loss: 1.4858... Generator Loss: 0.6128\n",
      "Epoch 1/1-Step 1140... Discriminator Loss: 1.7873... Generator Loss: 0.4608\n",
      "Epoch 1/1-Step 1150... Discriminator Loss: 1.5082... Generator Loss: 0.5438\n",
      "Epoch 1/1-Step 1160... Discriminator Loss: 1.6175... Generator Loss: 0.5056\n",
      "Epoch 1/1-Step 1170... Discriminator Loss: 1.7110... Generator Loss: 0.5155\n",
      "Epoch 1/1-Step 1180... Discriminator Loss: 1.8326... Generator Loss: 0.4454\n",
      "Epoch 1/1-Step 1190... Discriminator Loss: 1.5819... Generator Loss: 0.5531\n",
      "Epoch 1/1-Step 1200... Discriminator Loss: 1.5146... Generator Loss: 0.5342\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3856ce160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1210... Discriminator Loss: 1.6864... Generator Loss: 0.6659\n",
      "Epoch 1/1-Step 1220... Discriminator Loss: 1.6513... Generator Loss: 0.4822\n",
      "Epoch 1/1-Step 1230... Discriminator Loss: 1.6701... Generator Loss: 0.5620\n",
      "Epoch 1/1-Step 1240... Discriminator Loss: 1.6497... Generator Loss: 0.3909\n",
      "Epoch 1/1-Step 1250... Discriminator Loss: 1.5540... Generator Loss: 0.6141\n",
      "Epoch 1/1-Step 1260... Discriminator Loss: 1.6308... Generator Loss: 0.4989\n",
      "Epoch 1/1-Step 1270... Discriminator Loss: 1.6109... Generator Loss: 0.4821\n",
      "Epoch 1/1-Step 1280... Discriminator Loss: 1.6181... Generator Loss: 0.4695\n",
      "Epoch 1/1-Step 1290... Discriminator Loss: 1.6461... Generator Loss: 0.5510\n",
      "Epoch 1/1-Step 1300... Discriminator Loss: 1.5410... Generator Loss: 0.5102\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCrxkm7VpnAn8B98aQ1p
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383f66e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1310... Discriminator Loss: 1.6111... Generator Loss: 0.4807\n",
      "Epoch 1/1-Step 1320... Discriminator Loss: 1.7205... Generator Loss: 0.5266\n",
      "Epoch 1/1-Step 1330... Discriminator Loss: 1.6384... Generator Loss: 0.5218\n",
      "Epoch 1/1-Step 1340... Discriminator Loss: 1.6635... Generator Loss: 0.6340\n",
      "Epoch 1/1-Step 1350... Discriminator Loss: 1.6056... Generator Loss: 0.5998\n",
      "Epoch 1/1-Step 1360... Discriminator Loss: 1.5510... Generator Loss: 0.6269\n",
      "Epoch 1/1-Step 1370... Discriminator Loss: 1.5337... Generator Loss: 0.5658\n",
      "Epoch 1/1-Step 1380... Discriminator Loss: 1.5314... Generator Loss: 0.5803\n",
      "Epoch 1/1-Step 1390... Discriminator Loss: 1.5945... Generator Loss: 0.5902\n",
      "Epoch 1/1-Step 1400... Discriminator Loss: 1.6117... Generator Loss: 0.5127\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3856c8da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1410... Discriminator Loss: 1.6758... Generator Loss: 0.4837\n",
      "Epoch 1/1-Step 1420... Discriminator Loss: 1.6267... Generator Loss: 0.5655\n",
      "Epoch 1/1-Step 1430... Discriminator Loss: 1.5508... Generator Loss: 0.5399\n",
      "Epoch 1/1-Step 1440... Discriminator Loss: 1.6216... Generator Loss: 0.5033\n",
      "Epoch 1/1-Step 1450... Discriminator Loss: 1.5129... Generator Loss: 0.5440\n",
      "Epoch 1/1-Step 1460... Discriminator Loss: 1.5355... Generator Loss: 0.5692\n",
      "Epoch 1/1-Step 1470... Discriminator Loss: 1.7166... Generator Loss: 0.4328\n",
      "Epoch 1/1-Step 1480... Discriminator Loss: 1.5201... Generator Loss: 0.6278\n",
      "Epoch 1/1-Step 1490... Discriminator Loss: 1.5664... Generator Loss: 0.5372\n",
      "Epoch 1/1-Step 1500... Discriminator Loss: 1.5953... Generator Loss: 0.4794\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nE2jAO5tz0YXvgyqJZH+
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383a09940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1510... Discriminator Loss: 1.5750... Generator Loss: 0.5716\n",
      "Epoch 1/1-Step 1520... Discriminator Loss: 1.5316... Generator Loss: 0.6153\n",
      "Epoch 1/1-Step 1530... Discriminator Loss: 1.5014... Generator Loss: 0.5747\n",
      "Epoch 1/1-Step 1540... Discriminator Loss: 1.6495... Generator Loss: 0.4497\n",
      "Epoch 1/1-Step 1550... Discriminator Loss: 1.6587... Generator Loss: 0.4627\n",
      "Epoch 1/1-Step 1560... Discriminator Loss: 1.5300... Generator Loss: 0.5422\n",
      "Epoch 1/1-Step 1570... Discriminator Loss: 1.5563... Generator Loss: 0.5813\n",
      "Epoch 1/1-Step 1580... Discriminator Loss: 1.6175... Generator Loss: 0.6056\n",
      "Epoch 1/1-Step 1590... Discriminator Loss: 1.5851... Generator Loss: 0.5327\n",
      "Epoch 1/1-Step 1600... Discriminator Loss: 1.6242... Generator Loss: 0.4894\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3837ff9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1610... Discriminator Loss: 1.5834... Generator Loss: 0.5294\n",
      "Epoch 1/1-Step 1620... Discriminator Loss: 1.6298... Generator Loss: 0.5103\n",
      "Epoch 1/1-Step 1630... Discriminator Loss: 1.6286... Generator Loss: 0.5374\n",
      "Epoch 1/1-Step 1640... Discriminator Loss: 1.5931... Generator Loss: 0.5947\n",
      "Epoch 1/1-Step 1650... Discriminator Loss: 1.4749... Generator Loss: 0.5701\n",
      "Epoch 1/1-Step 1660... Discriminator Loss: 1.7635... Generator Loss: 0.4730\n",
      "Epoch 1/1-Step 1670... Discriminator Loss: 1.5748... Generator Loss: 0.4961\n",
      "Epoch 1/1-Step 1680... Discriminator Loss: 1.4538... Generator Loss: 0.5815\n",
      "Epoch 1/1-Step 1690... Discriminator Loss: 1.5649... Generator Loss: 0.5636\n",
      "Epoch 1/1-Step 1700... Discriminator Loss: 1.6351... Generator Loss: 0.5656\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8FwFlbmeWleUNNvoW3t
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383b4b400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1710... Discriminator Loss: 1.5923... Generator Loss: 0.5038\n",
      "Epoch 1/1-Step 1720... Discriminator Loss: 1.5411... Generator Loss: 0.5850\n",
      "Epoch 1/1-Step 1730... Discriminator Loss: 1.5569... Generator Loss: 0.5380\n",
      "Epoch 1/1-Step 1740... Discriminator Loss: 1.6375... Generator Loss: 0.5668\n",
      "Epoch 1/1-Step 1750... Discriminator Loss: 1.5744... Generator Loss: 0.5069\n",
      "Epoch 1/1-Step 1760... Discriminator Loss: 1.4704... Generator Loss: 0.5417\n",
      "Epoch 1/1-Step 1770... Discriminator Loss: 1.5687... Generator Loss: 0.5396\n",
      "Epoch 1/1-Step 1780... Discriminator Loss: 1.5869... Generator Loss: 0.5047\n",
      "Epoch 1/1-Step 1790... Discriminator Loss: 1.7663... Generator Loss: 0.4312\n",
      "Epoch 1/1-Step 1800... Discriminator Loss: 1.4238... Generator Loss: 0.6334\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383a0d240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1810... Discriminator Loss: 1.5628... Generator Loss: 0.5775\n",
      "Epoch 1/1-Step 1820... Discriminator Loss: 1.6301... Generator Loss: 0.5084\n",
      "Epoch 1/1-Step 1830... Discriminator Loss: 1.5955... Generator Loss: 0.5984\n",
      "Epoch 1/1-Step 1840... Discriminator Loss: 1.5123... Generator Loss: 0.6310\n",
      "Epoch 1/1-Step 1850... Discriminator Loss: 1.4927... Generator Loss: 0.5799\n",
      "Epoch 1/1-Step 1860... Discriminator Loss: 1.5843... Generator Loss: 0.6180\n",
      "Epoch 1/1-Step 1870... Discriminator Loss: 1.5226... Generator Loss: 0.5901\n",
      "Epoch 1/1-Step 1880... Discriminator Loss: 1.7258... Generator Loss: 0.4123\n",
      "Epoch 1/1-Step 1890... Discriminator Loss: 1.4861... Generator Loss: 0.5732\n",
      "Epoch 1/1-Step 1900... Discriminator Loss: 1.6749... Generator Loss: 0.5128\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO4f4eSFeFePnCQhkGEE
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd38383ea58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1910... Discriminator Loss: 1.5475... Generator Loss: 0.6490\n",
      "Epoch 1/1-Step 1920... Discriminator Loss: 1.5889... Generator Loss: 0.5187\n",
      "Epoch 1/1-Step 1930... Discriminator Loss: 1.4351... Generator Loss: 0.5514\n",
      "Epoch 1/1-Step 1940... Discriminator Loss: 1.6251... Generator Loss: 0.5055\n",
      "Epoch 1/1-Step 1950... Discriminator Loss: 1.6869... Generator Loss: 0.5026\n",
      "Epoch 1/1-Step 1960... Discriminator Loss: 1.6051... Generator Loss: 0.5311\n",
      "Epoch 1/1-Step 1970... Discriminator Loss: 1.5428... Generator Loss: 0.6195\n",
      "Epoch 1/1-Step 1980... Discriminator Loss: 1.5524... Generator Loss: 0.7520\n",
      "Epoch 1/1-Step 1990... Discriminator Loss: 1.5038... Generator Loss: 0.6006\n",
      "Epoch 1/1-Step 2000... Discriminator Loss: 1.4429... Generator Loss: 0.5845\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383568390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2010... Discriminator Loss: 1.6835... Generator Loss: 0.5229\n",
      "Epoch 1/1-Step 2020... Discriminator Loss: 1.6322... Generator Loss: 0.4383\n",
      "Epoch 1/1-Step 2030... Discriminator Loss: 1.5226... Generator Loss: 0.6373\n",
      "Epoch 1/1-Step 2040... Discriminator Loss: 1.5326... Generator Loss: 0.5994\n",
      "Epoch 1/1-Step 2050... Discriminator Loss: 1.6601... Generator Loss: 0.4407\n",
      "Epoch 1/1-Step 2060... Discriminator Loss: 1.4935... Generator Loss: 0.6701\n",
      "Epoch 1/1-Step 2070... Discriminator Loss: 1.4603... Generator Loss: 0.6488\n",
      "Epoch 1/1-Step 2080... Discriminator Loss: 1.5661... Generator Loss: 0.6325\n",
      "Epoch 1/1-Step 2090... Discriminator Loss: 1.5542... Generator Loss: 0.5592\n",
      "Epoch 1/1-Step 2100... Discriminator Loss: 1.4832... Generator Loss: 0.5962\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngQ79fEXTeqtl3n8IAiU
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383a0d358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2110... Discriminator Loss: 1.6266... Generator Loss: 0.4967\n",
      "Epoch 1/1-Step 2120... Discriminator Loss: 1.4847... Generator Loss: 0.6371\n",
      "Epoch 1/1-Step 2130... Discriminator Loss: 1.6838... Generator Loss: 0.6783\n",
      "Epoch 1/1-Step 2140... Discriminator Loss: 1.4585... Generator Loss: 0.6055\n",
      "Epoch 1/1-Step 2150... Discriminator Loss: 1.5668... Generator Loss: 0.6090\n",
      "Epoch 1/1-Step 2160... Discriminator Loss: 1.5711... Generator Loss: 0.5704\n",
      "Epoch 1/1-Step 2170... Discriminator Loss: 1.6091... Generator Loss: 0.4876\n",
      "Epoch 1/1-Step 2180... Discriminator Loss: 1.5204... Generator Loss: 0.5957\n",
      "Epoch 1/1-Step 2190... Discriminator Loss: 1.5270... Generator Loss: 0.6346\n",
      "Epoch 1/1-Step 2200... Discriminator Loss: 1.4999... Generator Loss: 0.4813\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nF9DOHep3GwLx25xkgv5
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383568b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2210... Discriminator Loss: 1.5486... Generator Loss: 0.5724\n",
      "Epoch 1/1-Step 2220... Discriminator Loss: 1.5582... Generator Loss: 0.5687\n",
      "Epoch 1/1-Step 2230... Discriminator Loss: 1.6047... Generator Loss: 0.4993\n",
      "Epoch 1/1-Step 2240... Discriminator Loss: 1.4981... Generator Loss: 0.5811\n",
      "Epoch 1/1-Step 2250... Discriminator Loss: 1.5765... Generator Loss: 0.5936\n",
      "Epoch 1/1-Step 2260... Discriminator Loss: 1.4636... Generator Loss: 0.5365\n",
      "Epoch 1/1-Step 2270... Discriminator Loss: 1.5911... Generator Loss: 0.5017\n",
      "Epoch 1/1-Step 2280... Discriminator Loss: 1.5097... Generator Loss: 0.5504\n",
      "Epoch 1/1-Step 2290... Discriminator Loss: 1.5114... Generator Loss: 0.6006\n",
      "Epoch 1/1-Step 2300... Discriminator Loss: 1.5466... Generator Loss: 0.4722\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFp47BAJP8whcqBvFmH1
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3838c4630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2310... Discriminator Loss: 1.5663... Generator Loss: 0.5642\n",
      "Epoch 1/1-Step 2320... Discriminator Loss: 1.5616... Generator Loss: 0.5305\n",
      "Epoch 1/1-Step 2330... Discriminator Loss: 1.5452... Generator Loss: 0.4717\n",
      "Epoch 1/1-Step 2340... Discriminator Loss: 1.5097... Generator Loss: 0.5591\n",
      "Epoch 1/1-Step 2350... Discriminator Loss: 1.5448... Generator Loss: 0.6524\n",
      "Epoch 1/1-Step 2360... Discriminator Loss: 1.5724... Generator Loss: 0.5009\n",
      "Epoch 1/1-Step 2370... Discriminator Loss: 1.5217... Generator Loss: 0.6050\n",
      "Epoch 1/1-Step 2380... Discriminator Loss: 1.4962... Generator Loss: 0.5783\n",
      "Epoch 1/1-Step 2390... Discriminator Loss: 1.5700... Generator Loss: 0.6142\n",
      "Epoch 1/1-Step 2400... Discriminator Loss: 1.4578... Generator Loss: 0.5684\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3831b4ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2410... Discriminator Loss: 1.5131... Generator Loss: 0.5926\n",
      "Epoch 1/1-Step 2420... Discriminator Loss: 1.5050... Generator Loss: 0.7034\n",
      "Epoch 1/1-Step 2430... Discriminator Loss: 1.5339... Generator Loss: 0.5644\n",
      "Epoch 1/1-Step 2440... Discriminator Loss: 1.4892... Generator Loss: 0.6411\n",
      "Epoch 1/1-Step 2450... Discriminator Loss: 1.4928... Generator Loss: 0.5780\n",
      "Epoch 1/1-Step 2460... Discriminator Loss: 1.4603... Generator Loss: 0.5994\n",
      "Epoch 1/1-Step 2470... Discriminator Loss: 1.5285... Generator Loss: 0.6405\n",
      "Epoch 1/1-Step 2480... Discriminator Loss: 1.4590... Generator Loss: 0.6900\n",
      "Epoch 1/1-Step 2490... Discriminator Loss: 1.5542... Generator Loss: 0.6006\n",
      "Epoch 1/1-Step 2500... Discriminator Loss: 1.4520... Generator Loss: 0.7350\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyFKyswIgw1IRaFhCdlV
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd383098780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2510... Discriminator Loss: 1.5680... Generator Loss: 0.5199\n",
      "Epoch 1/1-Step 2520... Discriminator Loss: 1.4815... Generator Loss: 0.5623\n",
      "Epoch 1/1-Step 2530... Discriminator Loss: 1.4943... Generator Loss: 0.6459\n",
      "Epoch 1/1-Step 2540... Discriminator Loss: 1.4227... Generator Loss: 0.6303\n",
      "Epoch 1/1-Step 2550... Discriminator Loss: 1.4773... Generator Loss: 0.6954\n",
      "Epoch 1/1-Step 2560... Discriminator Loss: 1.5832... Generator Loss: 0.6401\n",
      "Epoch 1/1-Step 2570... Discriminator Loss: 1.5105... Generator Loss: 0.6780\n",
      "Epoch 1/1-Step 2580... Discriminator Loss: 1.5102... Generator Loss: 0.5282\n",
      "Epoch 1/1-Step 2590... Discriminator Loss: 1.5095... Generator Loss: 0.5546\n",
      "Epoch 1/1-Step 2600... Discriminator Loss: 1.4809... Generator Loss: 0.5799\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQZpuHOocxVI3DcXKtFj
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382b7f978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2610... Discriminator Loss: 1.4600... Generator Loss: 0.6504\n",
      "Epoch 1/1-Step 2620... Discriminator Loss: 1.5427... Generator Loss: 0.6289\n",
      "Epoch 1/1-Step 2630... Discriminator Loss: 1.6130... Generator Loss: 0.5409\n",
      "Epoch 1/1-Step 2640... Discriminator Loss: 1.5036... Generator Loss: 0.5403\n",
      "Epoch 1/1-Step 2650... Discriminator Loss: 1.5142... Generator Loss: 0.6227\n",
      "Epoch 1/1-Step 2660... Discriminator Loss: 1.4610... Generator Loss: 0.6740\n",
      "Epoch 1/1-Step 2670... Discriminator Loss: 1.5189... Generator Loss: 0.6304\n",
      "Epoch 1/1-Step 2680... Discriminator Loss: 1.4857... Generator Loss: 0.5588\n",
      "Epoch 1/1-Step 2690... Discriminator Loss: 1.4780... Generator Loss: 0.6343\n",
      "Epoch 1/1-Step 2700... Discriminator Loss: 1.4853... Generator Loss: 0.6399\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIE2gIi4EqwjheKJOiEj
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382deb3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2710... Discriminator Loss: 1.4660... Generator Loss: 0.5470\n",
      "Epoch 1/1-Step 2720... Discriminator Loss: 1.6547... Generator Loss: 0.5747\n",
      "Epoch 1/1-Step 2730... Discriminator Loss: 1.6056... Generator Loss: 0.5382\n",
      "Epoch 1/1-Step 2740... Discriminator Loss: 1.5344... Generator Loss: 0.6217\n",
      "Epoch 1/1-Step 2750... Discriminator Loss: 1.4596... Generator Loss: 0.6441\n",
      "Epoch 1/1-Step 2760... Discriminator Loss: 1.5443... Generator Loss: 0.5064\n",
      "Epoch 1/1-Step 2770... Discriminator Loss: 1.5773... Generator Loss: 0.5066\n",
      "Epoch 1/1-Step 2780... Discriminator Loss: 1.5259... Generator Loss: 0.6386\n",
      "Epoch 1/1-Step 2790... Discriminator Loss: 1.5320... Generator Loss: 0.5977\n",
      "Epoch 1/1-Step 2800... Discriminator Loss: 1.5102... Generator Loss: 0.6136\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382dc10b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2810... Discriminator Loss: 1.4854... Generator Loss: 0.6670\n",
      "Epoch 1/1-Step 2820... Discriminator Loss: 1.5372... Generator Loss: 0.6835\n",
      "Epoch 1/1-Step 2830... Discriminator Loss: 1.5328... Generator Loss: 0.7039\n",
      "Epoch 1/1-Step 2840... Discriminator Loss: 1.6758... Generator Loss: 0.5030\n",
      "Epoch 1/1-Step 2850... Discriminator Loss: 1.3977... Generator Loss: 0.6095\n",
      "Epoch 1/1-Step 2860... Discriminator Loss: 1.6221... Generator Loss: 0.5926\n",
      "Epoch 1/1-Step 2870... Discriminator Loss: 1.5251... Generator Loss: 0.6481\n",
      "Epoch 1/1-Step 2880... Discriminator Loss: 1.4604... Generator Loss: 0.5213\n",
      "Epoch 1/1-Step 2890... Discriminator Loss: 1.5202... Generator Loss: 0.5007\n",
      "Epoch 1/1-Step 2900... Discriminator Loss: 1.4630... Generator Loss: 0.6140\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwIAmgYzvUos9UffpV7y
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382b66f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2910... Discriminator Loss: 1.3858... Generator Loss: 0.6565\n",
      "Epoch 1/1-Step 2920... Discriminator Loss: 1.4337... Generator Loss: 0.7014\n",
      "Epoch 1/1-Step 2930... Discriminator Loss: 1.5349... Generator Loss: 0.5818\n",
      "Epoch 1/1-Step 2940... Discriminator Loss: 1.3969... Generator Loss: 0.6059\n",
      "Epoch 1/1-Step 2950... Discriminator Loss: 1.5284... Generator Loss: 0.6069\n",
      "Epoch 1/1-Step 2960... Discriminator Loss: 1.4804... Generator Loss: 0.5743\n",
      "Epoch 1/1-Step 2970... Discriminator Loss: 1.4891... Generator Loss: 0.7033\n",
      "Epoch 1/1-Step 2980... Discriminator Loss: 1.5484... Generator Loss: 0.5540\n",
      "Epoch 1/1-Step 2990... Discriminator Loss: 1.5461... Generator Loss: 0.6244\n",
      "Epoch 1/1-Step 3000... Discriminator Loss: 1.4820... Generator Loss: 0.6587\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382663278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3010... Discriminator Loss: 1.5879... Generator Loss: 0.5517\n",
      "Epoch 1/1-Step 3020... Discriminator Loss: 1.4577... Generator Loss: 0.6005\n",
      "Epoch 1/1-Step 3030... Discriminator Loss: 1.4562... Generator Loss: 0.6765\n",
      "Epoch 1/1-Step 3040... Discriminator Loss: 1.5195... Generator Loss: 0.6498\n",
      "Epoch 1/1-Step 3050... Discriminator Loss: 1.4982... Generator Loss: 0.6116\n",
      "Epoch 1/1-Step 3060... Discriminator Loss: 1.4935... Generator Loss: 0.6510\n",
      "Epoch 1/1-Step 3070... Discriminator Loss: 1.5386... Generator Loss: 0.5516\n",
      "Epoch 1/1-Step 3080... Discriminator Loss: 1.4903... Generator Loss: 0.6672\n",
      "Epoch 1/1-Step 3090... Discriminator Loss: 1.5832... Generator Loss: 0.5869\n",
      "Epoch 1/1-Step 3100... Discriminator Loss: 1.4703... Generator Loss: 0.6179\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWD9b8TPL7BjakZN7kax
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd382d80b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3110... Discriminator Loss: 1.5633... Generator Loss: 0.5450\n",
      "Epoch 1/1-Step 3120... Discriminator Loss: 1.5169... Generator Loss: 0.5910\n",
      "Epoch 1/1-Step 3130... Discriminator Loss: 1.5457... Generator Loss: 0.6699\n",
      "Epoch 1/1-Step 3140... Discriminator Loss: 1.4988... Generator Loss: 0.5929\n",
      "Epoch 1/1-Step 3150... Discriminator Loss: 1.4436... Generator Loss: 0.6491\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-94-9e4afd78e3c4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGraph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m     train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,\n\u001b[0;32m---> 15\u001b[0;31m           celeba_dataset.shape, celeba_dataset.image_mode)\n\u001b[0m",
      "\u001b[0;32m<ipython-input-13-10fc281759f1>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode)\u001b[0m\n\u001b[1;32m     77\u001b[0m                 _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n\u001b[1;32m     78\u001b[0m                                                      \u001b[0minputs_real\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 79\u001b[0;31m                                                      lr_rate: learning_rate})\n\u001b[0m\u001b[1;32m     80\u001b[0m                 _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n\u001b[1;32m     81\u001b[0m                                                      \u001b[0minputs_real\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch_images\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 789\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    790\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    995\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    996\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 997\u001b[0;31m                              feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m    998\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    999\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1130\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1131\u001b[0m       return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1132\u001b[0;31m                            target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m   1133\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1134\u001b[0m       return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1137\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1140\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1119\u001b[0m         return tf_session.TF_Run(session, options,\n\u001b[1;32m   1120\u001b[0m                                  \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1121\u001b[0;31m                                  status, run_metadata)\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "batch_size = 64\n",
    "z_dim = 100\n",
    "learning_rate = 0.0002\n",
    "beta1 = 0.5\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "epochs = 1\n",
    "\n",
    "celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))\n",
    "with tf.Graph().as_default():\n",
    "    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,\n",
    "          celeba_dataset.shape, celeba_dataset.image_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
   ]
  }
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